Azure Data Fundamentals Exam Dumps and DP-900 Braindumps

Which Azure service provides a fully managed serverless Apache Spark runtime for processing large scale analytics and training machine learning models against data stored in Azure Cosmos DB?

• ❏ A. Azure Functions

• ❏ B. Azure Machine Learning

• ❏ C. Azure HDInsight

• ❏ D. Azure Databricks

Identify the missing word or words in the following statement for a Contoso Cloud analytics environment. In Synapse SQL analytics workloads run using a [?]. With a [?] the control node and compute nodes in the cluster run a variant of Azure SQL Database that supports distributed queries and you define your logic with Transact SQL statements?

• ❏ A. Synapse Spark pool

• ❏ B. Synapse Studio

• ❏ C. Synapse Pipeline

• ❏ D. BigQuery

• ❏ E. Synapse SQL pool

Which term best completes this sentence in the Azure cloud environment A(n) [?] is responsible for designing implementing maintaining and operating both on premises and cloud based database systems that use Azure data services and SQL Server and they are accountable for the systems availability steady performance and ongoing optimizations?

• ❏ A. Azure Data Engineer

• ❏ B. Azure Data Analyst

• ❏ C. Azure Database Administrator

• ❏ D. Azure Data Architect

Which service grouping in Contoso Cloud provides blob object storage message queuing table style key value storage and network file shares while offering extreme durability of up to 10 nines and scalability into hundreds of petabytes?

• ❏ A. Google Cloud Storage

• ❏ B. Contoso Blob Storage

• ❏ C. Contoso Storage Account

• ❏ D. Contoso Disk Storage

Which role is mainly responsible for making large or complex datasets more understandable and usable while creating charts maps and other visualizations and transforming and integrating data from multiple sources to deliver rich dashboards and reports?

• ❏ A. Google Cloud Program Manager

• ❏ B. Google Cloud Database Administrator

• ❏ C. Google Cloud Data Analyst

• ❏ D. Google Cloud Data Engineer

Your development team must host large media assets that the web front ends will retrieve often. Which Azure storage service should you select?

• ❏ A. Azure Files

• ❏ B. Azure Blob Storage

• ❏ C. Azure Disk Storage

• ❏ D. Azure Queue Storage

Within the context of Contoso Cloud identify the missing word or words in the following sentence. A(n) [?] is a unit of code that runs inside your database engine and applications often use a [?] because it is optimized to execute within the database environment and can retrieve data very quickly?

• ❏ A. User defined function

• ❏ B. System catalog

• ❏ C. Linked server

• ❏ D. Stored procedure

A regional bank runs a critical application on an Azure SQL Database and you must ensure the database remains available during platform outages. Which option provides the highest availability level for Azure SQL Database?

• ❏ A. Hyperscale service tier

• ❏ B. General Purpose service tier

• ❏ C. Basic service tier

• ❏ D. Business Critical service tier

Which file system underpins Contoso Data Lake Storage Gen2 and provides Hadoop compatibility for high throughput and fault tolerant operations?

• ❏ A. NTFS

• ❏ B. Hadoop Distributed File System HDFS

• ❏ C. FAT32

• ❏ D. Google Cloud Storage

A regional retail analytics firm uses Azure Cosmos DB and they require that a write becomes visible to clients only after every replica has acknowledged the change and they cannot enable this mode if the data must be replicated across multiple global regions. Which consistency level enforces that behavior?

• ❏ A. Session consistency

• ❏ B. Consistent prefix consistency

• ❏ C. Eventual consistency

• ❏ D. Strong consistency

• ❏ E. Bounded staleness consistency

A regional payments firm is assigning duties for cloud hosted databases and needs to define operational roles. Which of the following is not typically a responsibility of a database administrator for cloud data services?

• ❏ A. Monitoring database performance and managing resource utilization

• ❏ B. Configuring Google Cloud IAM roles and project level access policies

• ❏ C. Designing and coding application business logic and user interface components

• ❏ D. Scheduling backups and orchestrating disaster recovery procedures

A retail site named BlueCart performs frequent reads and writes against a relational Cloud SQL instance to process orders and update customer records. What category of data processing does this application use?

• ❏ A. Stream processing

• ❏ B. Online Analytical Processing (OLAP)

• ❏ C. Batch processing

• ❏ D. Online Transaction Processing (OLTP)

A regional credit union plans to deploy a data catalog to help analysts find and interpret data assets hosted in Azure. Which Azure service is intended to provide that functionality?

• ❏ A. Azure Synapse Analytics

• ❏ B. Azure HDInsight

• ❏ C. Microsoft Purview

• ❏ D. Azure Data Factory

How does Azure Synapse Analytics serve as a contemporary data warehousing solution that supports enterprise analytics and integrated data processing?

• ❏ A. Enable automatic collection of data from diverse sources for processing

• ❏ B. Power BI

• ❏ C. Offer an integrated analytics environment combining enterprise data warehousing with big data processing and data integration

• ❏ D. Act as a storage service for very large unstructured files

Meridian Retail is shifting a sizable on premises Apache Cassandra cluster to Microsoft Azure. Which Azure service provides a managed Apache Cassandra compatible database service?

• ❏ A. Azure Cosmos DB

• ❏ B. Google Cloud Bigtable

• ❏ C. Azure Database for Apache Cassandra

• ❏ D. Azure Cache for Redis

Fill in the missing terms in this description for a Microsoft Azure analytics platform. Northwind Analytics is a unified analytics service that can ingest data from a variety of sources, transform the data, produce analytics and models, and persist the outputs. You can choose between two processing engines. [A] uses the same SQL dialect as Azure SQL Database and provides extensions for accessing external data such as databases, files, and Azure Data Lake Storage. [B] is the open source engine that powers Azure Databricks and is commonly used with notebooks in languages such as C#, Scala, Python, or SQL. What are the missing terms?

• ❏ A. BigQuery and Dataproc

• ❏ B. HiveQL and Flink

• ❏ C. Transact-SQL and Spark

• ❏ D. T-SQL and Hadoop

A transportation analytics startup has noticed that the rise of connected sensors and constant internet connectivity has dramatically increased the amount of data that systems can produce and analyze, and much of this information is generated continuously and can be processed as it flows or nearly in real time. Which of the following best describes stream processing?

• ❏ A. Several incoming records are buffered and then processed together in one operation

• ❏ B. Cloud Dataflow

• ❏ C. New data events are processed continuously as they arrive

• ❏ D. Data is gathered into a temporary store and all records are processed together as a batch

A regional retailer uses a hosted database service named Horizon SQL Database for analytics and reporting. What is the main purpose of the query store feature?

• ❏ A. Cloud SQL Insights

• ❏ B. To schedule and manage automated database backups

• ❏ C. To persist historical query performance metrics for trend analysis

• ❏ D. To cache frequently executed statements for faster response

Which approach to provisioning a NoSQL datastore involves authoring a JSON declaration file that is stored in version control and is treated as Infrastructure as Code?

• ❏ A. Google Cloud Deployment Manager

• ❏ B. PowerShell scripts

• ❏ C. Azure Portal

• ❏ D. Azure Resource Manager templates

Which three elements can be placed on a DataVista Studio dashboard to present data and context? (Choose 3)

• ❏ A. A presentation slide

• ❏ B. A text box

• ❏ C. A complete report page

• ❏ D. An individual visualization from a report

In a relational database design you model real world entities as tables and each row captures one instance of an entity. For example a neighborhood marketplace might define tables for clients products orders and order_line_entries and each attribute is separated into its own place. On which element of a relational table do you assign a datatype to constrain the values that can be stored?

• ❏ A. Relationships

• ❏ B. Fields

• ❏ C. Tables

• ❏ D. Columns

• ❏ E. Rows

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

For a system that must hold JSON records without a fixed schema while providing flexible indexing and querying capabilities which Azure service is most suitable?

• ❏ A. Azure Blob Storage

• ❏ B. Azure SQL Database

• ❏ C. Azure Table Storage

• ❏ D. Azure Cosmos DB

In a managed Azure SQL instance supporting a supply chain analytics team at Halcyon Logistics what purpose does the tempdb system database serve during query processing?

• ❏ A. To record transactional history and durable log records

• ❏ B. To maintain user account credentials and access control

• ❏ C. To persist user created tables and stored procedures

• ❏ D. To hold temporary tables table variables and intermediate result sets used while queries run

Which analytics category is used when a report only summarizes prior events using historical records and does not attempt to predict or prescribe actions?

• ❏ A. Predictive analytics

• ❏ B. Prescriptive analytics

• ❏ C. Descriptive analytics

• ❏ D. Diagnostic analytics

How would you describe a security principal in a cloud environment like NimbusCloud when determining whether a request should be permitted?

• ❏ A. A named set of permissions that can be assigned to identities such as roles like Owner or Editor

• ❏ B. A collection of resources grouped under a project to which access controls can be applied

• ❏ C. A policy binding that attaches a role to a specific member within an IAM policy

• ❏ D. An identity object that represents a user group service account or managed identity that is requesting access to cloud resources

Which term completes the following statement about Azure Cosmos DB by ensuring that updates are seen in order even though there can be a delay before they become visible and during that delay clients may observe stale data?

• ❏ A. Bounded Staleness

• ❏ B. Session

• ❏ C. Strong

• ❏ D. Consistent Prefix

• ❏ E. Eventual

Which term refers to handling multiple records together as a collection rather than processing each record one by one?

• ❏ A. Stream processing

• ❏ B. Buffering

• ❏ C. Batch processing

• ❏ D. Template rendering

Which sequence of steps represents the typical workflow when using Power BI?

• ❏ A. Create a report in the Power BI service then send it to the Power BI mobile app and open it in Power BI Desktop

• ❏ B. Load data into Power BI Desktop, design reports, publish to the Power BI service and then view and interact in the service and mobile

• ❏ C. Import data into Power BI mobile, build the report there and then export it to Power BI Desktop

• ❏ D. Author paginated reports in Power BI Report Builder then publish them and try to edit the original dataset in Power BI Desktop

A regional insurance firm runs many Azure SQL databases that require recurring work such as backups, rebuilding indexes, and updating statistics and they want to automate those tasks across every database. Which Azure service can they use to create, schedule, and manage runbooks to perform these maintenance jobs?

• ❏ A. Azure Logic Apps

• ❏ B. Azure Event Grid

• ❏ C. Azure Functions

• ❏ D. Azure Automation

At DataWave Analytics what does the term “schema on read” mean when working with a data lake?

• ❏ A. Data is encrypted on arrival to ensure confidentiality

• ❏ B. A strict schema is enforced at ingest which rejects records that do not match

• ❏ C. Metadata and a schema are automatically derived and stored when the data is loaded

• ❏ D. The data is stored raw and structure is applied only when the data is read

Which Cosmos DB API is most appropriate for storing JSON documents and performing SQL style queries against those records?

• ❏ A. MongoDB API

• ❏ B. Gremlin graph API

• ❏ C. Core SQL API

• ❏ D. Cassandra API

Identify the missing term in the following sentence when discussing Contoso Cloud. [?] describes datasets that are too large or complex for conventional database systems. Systems that handle [?] must support fast data ingestion and processing and they must provide storage for results and enough compute to run analytics over those results?

• ❏ A. Apache Beam

• ❏ B. Google BigQuery

• ❏ C. Ceph

• ❏ D. Hadoop

• ❏ E. Cloud Pub/Sub

• ❏ F. Big data

A data team at Nimbus Analytics must pair each database utility with the correct description based on how the tools are typically used. Which pairing accurately assigns both tools to their functions?

• ❏ A. Azure Data Studio is a graphical administrative console for server configuration and instance management and SQL Server Management Studio (SSMS) is a lightweight cross platform editor for executing on demand queries and exporting results

• ❏ B. SQL Server Management Studio (SSMS) is a graphical management console for administering SQL Server and Azure SQL instances and it supports configuration management and administrative tasks Azure Data Studio is a lightweight cross platform editor for running ad hoc queries and saving results as text JSON or spreadsheet formats

• ❏ C. SQL Server Management Studio (SSMS) is a development suite for authoring Analysis Integration and Reporting Services projects and Azure Data Studio is a full fledged IDE for building complete database solutions

• ❏ D. Azure Data Studio is a Microsoft extension that enables direct querying of BigQuery datasets and SQL Server Management Studio (SSMS) is a Cloud Console plugin for managing Cloud SQL instances

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

Within the context of Contoso Cloud data solutions data processing converts raw inputs into useful information through ordered operations and depending on how data enters the platform you can handle each record as it arrives or accumulate raw records and process them together. What is the term for collecting records and processing them in grouped units?

• ❏ A. Data ingestion

• ❏ B. Batch processing

• ❏ C. Cloud Pub/Sub

• ❏ D. Streaming

A retail analytics firm called Meridian Insights needs a managed service to build and deploy serverless scalable APIs that serve machine learning models for instantaneous predictions. Which Google Cloud service should they use?

• ❏ A. Cloud Run

• ❏ B. Vertex AI

• ❏ C. Google Kubernetes Engine

• ❏ D. Cloud Functions

Modern services must remain highly responsive and continuously available for users. To meet low latency and high availability requirements they are deployed near customer regions and must scale automatically during traffic surges while retaining growing volumes of data and returning results in milliseconds. Which Azure storage offering lets you aggregate JSON document data for analytical reports without extra development effort?

• ❏ A. Azure SQL Database

• ❏ B. Azure Data Lake Storage

• ❏ C. Azure Cosmos DB

• ❏ D. Azure Blob Storage

When a primary key is defined on a table in a relational database what consequence does it impose on that table?

• ❏ A. The table enforces that each primary key value is unique so no two rows share the same key

• ❏ B. Every table is required to declare exactly one primary key

• ❏ C. A relational table can hold multiple primary keys

• ❏ D. Defining a primary key forces all rows in a partition to share the same key value

How does storing data by columns improve analytical workload performance in cloud data platforms like Contoso Analytics?

• ❏ A. Reduces storage costs through higher compression rates

• ❏ B. Provides stronger controls for protecting sensitive fields

• ❏ C. Improves analytical query speed by reading only the columns that are needed

• ❏ D. Increases the speed of ingesting data from many sources

Which Microsoft Azure service enables an analyst at Meridian Retail to create tabular models for online analytical processing queries by combining data from multiple sources and embedding business rules into the model?

• ❏ A. Azure Synapse Analytics

• ❏ B. Azure Analysis Services

• ❏ C. Azure Cosmos DB

• ❏ D. Azure SQL Database

• ❏ E. Azure Data Lake Storage

Which cloud service model does NebulaDB hosted by Stratus Cloud represent?

• ❏ A. Software as a service

• ❏ B. Infrastructure as a service

• ❏ C. Platform as a service managed offering

A data engineering team at Meridian Retail keeps customer and order information in separate tables. What mechanism in a relational database enforces and maintains the relationships between those tables?

• ❏ A. Table partitions

• ❏ B. Columns

• ❏ C. Indexes

• ❏ D. Key constraints

Fill in the blank in the following sentence about a cloud platform. [?] is a managed data integration service that lets you create data driven workflows to orchestrate data movement and to transform data at scale, and using [?] you can compose and schedule pipelines that ingest from multiple data sources and author visual data flows for complex ETL tasks?

• ❏ A. Azure PowerShell

• ❏ B. Cloud Data Fusion

• ❏ C. Azure Data Factory

• ❏ D. Azure Portal

An engineer at Maple Trail Software runs Transact SQL scripts against an Azure SQL Database instance. What is the role of the GO statement inside a Transact SQL script?

• ❏ A. Invoke a stored procedure to execute its logic

• ❏ B. Start a transaction that spans multiple commands

• ❏ C. Mark the end of a batch of statements

• ❏ D. Convert several lines into a commented out block

When discussing the Contoso cloud platform which phrase completes the sentence if “[?]” is defined as “general purpose object storage for large binary files that fits any scenario”?

• ❏ A. Contoso Queue Storage

• ❏ B. Contoso Disk Storage

• ❏ C. Contoso Blob Storage

• ❏ D. Contoso File Storage

Which SQL statement is an example of the Data Definition Language that is used to create or modify database schema objects?

• ❏ A. MERGE

• ❏ B. JOIN

• ❏ C. CREATE TABLE

• ❏ D. SELECT

Which of the following examples would be considered structured data? (Choose 2)

• ❏ A. Cloud Storage object

• ❏ B. Relational database table

• ❏ C. JSON document

• ❏ D. Delimited spreadsheet file

For a managed instance of Azure SQL Database that is used by a mid sized retailer called Orion Retail what is the simplest method to ensure backups are performed?

• ❏ A. Create a scheduled SQL Agent job to perform database backups

• ❏ B. Automated backups provided by the managed instance

• ❏ C. Manually export database backups to an Azure Storage account

• ❏ D. Azure Backup

When provisioning a NebulaDB account which capability ensures replicas are distributed across multiple availability zones within a single region?

• ❏ A. Multi-master replication

• ❏ B. Regional Persistent Disk

• ❏ C. Availability Zones

• ❏ D. Strong consistency model

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

In Contoso’s cloud table service what two properties compose the storage key that is used to locate a specific entity?

• ❏ A. Project ID and table ID

• ❏ B. Private key and public key

• ❏ C. Partition key and row key

• ❏ D. Table name and column name

A retail analytics team at Northwind Cloud needs to load data from multiple on premises systems and cloud sources into Azure Synapse Analytics. Which Azure service should they use to perform the data ingestion?

• ❏ A. Azure Event Hubs

• ❏ B. Power BI

• ❏ C. Azure Data Factory

• ❏ D. Azure Databricks

In a cloud environment such as Contoso Cloud what does the term “data masking” mean and why is it applied?

• ❏ A. Cloud DLP

• ❏ B. A technique for reducing dataset size to lower storage costs

• ❏ C. It is the practice of replacing or obscuring sensitive fields so they remain usable for testing and analytics

• ❏ D. A method of encrypting confidential information to protect privacy

A facilities team at Summit Environments stores streaming telemetry in an Azure Cosmos DB container named telemetry_store and each document includes a recorded_time attribute. You need to add a secondary index on that attribute to improve performance for queries that filter or sort by time intervals. Which index type should you create?

• ❏ A. Spatial index

• ❏ B. Range index

• ❏ C. Hash index

• ❏ D. Unique index

True or false The Database Migration Utility lets you restore a backup from your on-site databases directly into database instances hosted on Nimbus Cloud Services and you must suspend all on-site write activity during the final cutover to prevent data inconsistencies?

• ❏ A. False

• ❏ B. True

Identify the missing word or words in the following sentence as presented in a Contoso Cloud fundamentals example. A(n) [?] system is built to help business stakeholders run queries and obtain a strategic view of the data stored in a database and A(n) [?] system focuses on collecting raw information and turning it into insights that inform business decisions?

• ❏ A. Qualitative

• ❏ B. Operational

• ❏ C. Analytical

• ❏ D. Transactional

At Aurora Logistics when ingesting streaming telemetry and batch transaction files is it impossible to remove or filter out corrupted or duplicate records until after the data has been persisted?

• ❏ A. True

• ❏ B. False

The correct answer is Azure Databricks.

Azure Databricks provides a fully managed serverless Apache Spark runtime that is optimized for large scale analytics and for training machine learning models. It integrates with Azure services and supports the Cosmos DB Spark connector so you can process and train models directly against data stored in Azure Cosmos DB.

Azure Functions is a serverless compute service for event driven and short lived workloads and it does not provide a managed Apache Spark runtime for large scale analytics or model training.

Azure Machine Learning is a managed service for building, training, and deploying machine learning models but it does not deliver a native fully managed serverless Apache Spark runtime as Databricks does.

Azure HDInsight offers managed Hadoop and Spark clusters but it relies on provisioned clusters rather than a fully managed serverless Spark runtime and it is not the service referenced by the question.

When a question mentions a fully managed serverless Apache Spark runtime for analytics and machine learning look for options that explicitly mention Databricks or serverless Spark and eliminate general purpose serverless compute and provisioned cluster services.

The correct option is Synapse SQL pool.

A Synapse SQL pool provides a control node and multiple compute nodes and it runs a variant of Azure SQL Database that supports distributed queries and you define your logic using Transact SQL statements. The control node coordinates query planning and distribution and the compute nodes execute the distributed work in parallel which matches the architecture described in the question.

Synapse Spark pool is incorrect because Spark pools run Apache Spark workloads and they use Spark SQL Scala or Python rather than Transact SQL and they do not provide the same SQL control node and compute node distributed architecture.

Synapse Studio is incorrect because Studio is the web authoring and management workspace and it is not a compute engine that executes distributed SQL queries.

Synapse Pipeline is incorrect because pipelines orchestrate data movement and workflows and they do not provide the SQL-based distributed compute cluster described in the question.

BigQuery is incorrect because BigQuery is a Google Cloud analytics service and it is not an Azure Synapse offering and the question is describing an Azure Synapse SQL pool architecture.

When a question mentions a control node and compute nodes and Transact SQL look for references to a Synapse SQL pool or dedicated SQL pool rather than Spark or pipeline services.

Azure Database Administrator is the correct option.

This role matches the description because it focuses on designing, implementing, maintaining and operating both on premises and cloud based database systems that use Azure data services and SQL Server, and it is accountable for system availability, steady performance, and ongoing optimizations.

The database administrator handles tasks such as backups and recovery, patching and upgrades, performance tuning, high availability, disaster recovery planning and operational security, and those responsibilities align directly with the sentence in the question.

Azure Data Engineer is incorrect because data engineers primarily build and manage data pipelines and transformation processes rather than being responsible for the operational availability and tuning of database systems.

Azure Data Analyst is incorrect because analysts focus on interpreting data and creating reports and insights rather than on administering and optimizing database platforms.

Azure Data Architect is incorrect because architects design data solutions and overall strategy rather than being accountable for day to day operations, maintenance, and steady performance of database systems.

Look for operational verbs like maintaining, operating, and ensuring availability to identify a role that is focused on ongoing system administration such as the Database Administrator.

The correct option is Contoso Storage Account.

Contoso Storage Account provides blob object storage, message queuing, table style key value storage, and network file shares which directly matches the services listed in the question. It is designed to deliver extremely high durability up to ten nines depending on replication choices and to scale into hundreds of petabytes per account so it meets both the durability and scalability requirements described.

A storage account groups multiple storage services under a single management and billing boundary which makes it the right choice when a single grouping must offer blobs, queues, tables, and files. That unified model is why Contoso Storage Account is the correct answer.

Google Cloud Storage is a public cloud object storage service and not the Contoso grouping described in the question. It does not represent the Contoso account model that includes queues, tables, and file shares under the same service boundary.

Contoso Blob Storage only addresses object blob storage and therefore it cannot satisfy the requirement for message queuing, table key value storage, and network file shares that the question lists.

Contoso Disk Storage refers to block level disks for virtual machines and it is not intended for object storage, messaging queues, table style key value stores, or network file shares so it does not meet the stated requirements.

Focus on keyword groups like blob, queue, table, file, durability, and scalability and pick the option that explicitly groups all of those services together.

The correct answer is Google Cloud Data Analyst.

The Google Cloud Data Analyst role is focused on making large and complex datasets understandable and usable while creating charts, maps and other visualizations and transforming and integrating data from multiple sources to deliver rich dashboards and reports. Analysts typically use SQL, BigQuery and visualization tools to clean, aggregate and present data so that stakeholders can make informed decisions.

Google Cloud Program Manager is incorrect because that role is centered on coordinating projects and programs and managing timelines and stakeholders rather than producing visualizations or integrating data for reporting.

Google Cloud Database Administrator is incorrect because that role concentrates on installing, configuring and maintaining database systems, ensuring performance and backups, and managing schemas rather than designing dashboards and visualizations for analysis.

Google Cloud Data Engineer is incorrect because data engineers build and maintain data pipelines and infrastructure and they focus on scalable data processing and integration. Data engineers enable analytics but the primary responsibility for transforming data into dashboards and visualizations belongs to the data analyst.

When you see keywords like charts, maps, visualizations and dashboards look for the role that emphasizes analysis and presentation of data rather than infrastructure or project management.

The correct answer is Azure Blob Storage.

Azure Blob Storage is purpose built for storing large amounts of unstructured data such as images and video and it is optimized for HTTP access so web front ends can retrieve media efficiently. It offers scalable throughput and bandwidth and integrates with CDNs and cost optimized access tiers for frequently or infrequently accessed content.

Azure Files provides managed SMB and NFS file shares and it is useful for lift and shift scenarios and shared file systems. It is not the best choice when you need to serve large public media at scale to web clients compared to Blob Storage.

Azure Disk Storage delivers block level disks for virtual machines and it is intended for attaching OS and data disks to VMs. It is not designed for direct serving of large media objects to web front ends.

Azure Queue Storage is a messaging service used to decouple application components and manage asynchronous work. It is not used for storing or serving media assets.

When a question asks about hosting large media for frequent web retrieval look for services designed for unstructured object storage and direct HTTP access such as Blob Storage. Eliminate options that focus on file shares, VM disks, or messaging.

The correct answer is Stored procedure.

A Stored procedure is a reusable unit of procedural code that is stored in and executed by the database engine, and applications often call a Stored procedure because it runs close to the data and can retrieve or manipulate data very efficiently.

User defined function is incorrect because a user defined function is intended to return a value or a table and it is mainly used inside queries. User defined functions usually have restrictions on side effects and transaction control, and they are not typically used to encapsulate multi statement operational logic the way a stored procedure is.

System catalog is incorrect because the system catalog is a set of metadata tables or views that describe database objects. The system catalog does not contain executable procedural code and it does not run logic inside the engine.

Linked server is incorrect because a linked server is a configuration that allows a database to access external data sources on other servers. It is about connectivity and distributed queries and not about a unit of code that executes inside the database engine.

When a question asks about a unit of code that runs inside the database engine and is called by applications look for Stored procedure and eliminate choices that describe metadata or connectivity rather than executable code.

The correct option is Business Critical service tier.

Business Critical service tier provides the highest availability for Azure SQL Database because it uses a multiple synchronous replica architecture that enables fast automatic failover and lower recovery time. It places replicas on isolated hardware and uses local SSD storage to provide high IOPS and low latency which helps keep the database available during platform outages.

Business Critical service tier includes three synchronous replicas and supports zone resilient deployments to protect against datacenter or zone failures. Those built in high availability capabilities and the corresponding SLA make it the best choice for a critical banking application that must remain available during platform outages.

Hyperscale service tier is optimized for rapid growth of storage and for fast backups and restores. Its compute and storage separation gives excellent scalability but it is not designed around the synchronous replica Always On pattern that Business Critical uses for the highest platform availability.

General Purpose service tier uses remote storage with network based redundancy and is intended for cost effective availability. It provides good resilience for many workloads but it has higher recovery time objectives compared with Business Critical so it is not the highest availability tier.

Basic service tier is meant for development and small non critical workloads. It has minimal redundancy and a lower SLA so it cannot provide the level of availability required for a critical production database during platform outages.

When a question asks about highest availability look for tiers that mention synchronous replicas or automatic failover. Business Critical is the tier that provides these architectural features.

The correct answer is Hadoop Distributed File System HDFS.

Hadoop Distributed File System HDFS is a distributed, fault tolerant file system designed for big data workloads and it is what gives Azure Data Lake Storage Gen2 the HDFS compatibility that supports high throughput and integration with Hadoop ecosystem tools.

NTFS is a Windows local disk file system and it does not provide the distributed HDFS compatible semantics needed for Hadoop or for ADLS Gen2.

FAT32 is an older desktop file system with file size and metadata limitations and it is not suitable for distributed big data storage or Hadoop compatibility.

Google Cloud Storage is an object storage service from Google Cloud and it is not the file system that underpins Azure Data Lake Storage Gen2 nor is it a native HDFS implementation.

When a question asks about Hadoop compatibility focus on whether the service exposes HDFS semantics or HDFS APIs rather than on general purpose desktop file systems or cloud object stores.

Strong consistency is the correct option.

Strong consistency enforces linearizability so a write becomes visible to clients only after every replica has acknowledged the change. This is the strictest consistency level in Azure Cosmos DB and it guarantees that reads always observe the most recent committed write. In Azure Cosmos DB this strict mode is constrained by the account topology and it cannot be enabled when you require multi region write replication across global regions.

Session consistency is incorrect because it provides read your writes and monotonic reads within a single session but it does not require all replicas to acknowledge a write before it becomes visible globally.

Consistent prefix consistency is incorrect because it only guarantees that reads observe a prefix of writes in order and it does not force immediate acknowledgement from every replica for each write.

Eventual consistency is incorrect because it is the weakest level and allows replicas to converge over time so writes can become visible at different times on different replicas.

Bounded staleness consistency is incorrect because it allows reads to lag by a bounded amount of versions or time and it does not require every replica to acknowledge a write before that write becomes visible.

Watch for keywords like every replica and cannot be enabled with multiple global regions because they point to Strong consistency as the required behavior.

The correct answer is Designing and coding application business logic and user interface components.

Database administrators for cloud data services focus on operational duties such as schema design, query tuning, capacity planning, security, backups and recovery. Application business logic and user interface work belong to application developers and product teams, so Designing and coding application business logic and user interface components is not typically a DBA responsibility.

Monitoring database performance and managing resource utilization is a central DBA responsibility. DBAs monitor query performance, tune indexes, and manage instance sizing or autoscaling policies to meet performance and cost objectives.

Configuring Google Cloud IAM roles and project level access policies is often part of a DBA role when it concerns database access and permissions. DBAs define who can connect to and manage databases and they work with cloud administrators to apply appropriate IAM roles.

Scheduling backups and orchestrating disaster recovery procedures is a standard DBA duty. DBAs implement backup schedules, test restore procedures, and configure replication and failover to meet recovery objectives.

When deciding which choice is not a DBA task look for items about application feature development. Application coding and UI design are developer responsibilities while monitoring, access control, backups and DR are operational DBA tasks.

The correct answer is Online Transaction Processing (OLTP). This retail application performs frequent short reads and writes against a relational Cloud SQL instance to process orders and update customer records which matches the characteristics of OLTP.

OLTP workloads emphasize many small, concurrent transactions with low latency and strong consistency. A relational Cloud SQL instance is designed for these kinds of transactional operations where ACID guarantees and fast updates to customer and order data are required.

Stream processing is not correct because it focuses on continuous processing of event streams and real time analytics rather than the many small, transactional updates to a relational database that this scenario describes.

Online Analytical Processing (OLAP) is not correct because OLAP is optimized for complex, large scale analytical queries and reporting over historical data rather than low latency transactional reads and writes to update current records.

Batch processing is not correct because batch jobs run on large sets of data at scheduled intervals and are not suited for the immediate, per-transaction updates needed to process orders and keep customer records current.

When you see frequent, low latency reads and writes to a relational database look for OLTP. If the question mentions large scale analytics or scheduled jobs then consider OLAP or batch instead.

Microsoft Purview is the correct answer.

Microsoft Purview is built to provide a unified data catalog and governance layer for Azure. It can scan and classify data sources, maintain a business glossary, show data lineage, and provide search and discovery capabilities so analysts can find and interpret data assets across the environment.

Azure Synapse Analytics is an analytics platform that combines data warehousing and big data processing. It offers query and processing capabilities but it is not primarily a managed enterprise data catalog, and organizations commonly integrate Synapse with Purview for metadata and governance.

Azure HDInsight is a managed service for Hadoop, Spark and other open source big data frameworks. It is focused on running clusters and processing large data workloads and it does not provide the centralized cataloging and governance features that Purview provides.

Azure Data Factory is an orchestration and ETL service for moving and transforming data. It is used to build data pipelines and integrate systems, but it does not function as an enterprise data catalog with automated classification, glossary and lineage in the way Microsoft Purview does.

When a question asks about a data catalog or data governance choose Microsoft Purview and rule out services that are focused on processing or orchestration rather than metadata and discovery.

The correct answer is Offer an integrated analytics environment combining enterprise data warehousing with big data processing and data integration.

Azure Synapse Analytics is built as a unified platform that brings enterprise data warehousing together with big data processing and built in data integration. It combines dedicated and serverless SQL query engines with Apache Spark and pipeline orchestration so teams can ingest transform and analyze data in one integrated workspace. This unified design is what the bolded option describes and is the primary reason it fits the question.

Enable automatic collection of data from diverse sources for processing is incorrect because automatic collection and event ingestion are handled by services such as Azure Data Factory Event Hubs or other ingestion tools and not by a single definition of Synapse even though Synapse integrates with those tools.

Power BI is incorrect because Power BI is a separate service for reporting and visualization that can connect to Synapse but it is not the data warehousing and big data processing platform itself.

Act as a storage service for very large unstructured files is incorrect because storage of large unstructured data is the role of Azure Data Lake Storage or Blob Storage and Synapse is designed to process and query data stored there rather than serve primarily as raw object storage.

When you see an option that emphasizes the integration of data warehousing big data processing and data integration across a single analytics workspace it is a strong indicator of Azure Synapse Analytics.

Azure Database for Apache Cassandra is correct.

Azure Database for Apache Cassandra is the managed Azure service that provides native Apache Cassandra compatibility so you can migrate Cassandra clusters and continue to use Cassandra Query Language and existing Cassandra clients and tools while Azure handles the infrastructure management.

Azure Cosmos DB offers a Cassandra-compatible API which can run Cassandra-protocol workloads, but it is a different multi model service and the question expects the dedicated Azure managed Cassandra offering rather than the Cosmos DB multi model platform.

Google Cloud Bigtable is a Google Cloud Platform service and not an Azure product. It is a wide column store but it does not provide native Apache Cassandra CQL or wire protocol compatibility and so it is not the right Azure service for a Cassandra migration.

Azure Cache for Redis is an in memory key value and caching service. It does not implement Cassandra data models or the Cassandra protocol and it is not suitable as a managed Cassandra compatible database.

Pay attention to the cloud vendor in the question and pick the service that explicitly states Apache Cassandra compatibility. Azure Database for Apache Cassandra is the dedicated managed option on Azure for Cassandra migrations.

The correct option is Transact-SQL and Spark. The service exposes a Transact-SQL based engine for SQL oriented workloads and an Apache Spark engine for notebook based analytics.

The Transact-SQL engine uses the same SQL dialect as Azure SQL Database and it provides extensions such as PolyBase and OPENROWSET to access external data in databases, files, and Azure Data Lake Storage.

The Spark engine is the open source distributed analytics engine that powers Azure Databricks and it is commonly used from notebooks in languages like C#, Scala, Python, and SQL for large scale processing and machine learning.

BigQuery and Dataproc is incorrect because BigQuery is a Google Cloud data warehouse and Dataproc is a Google managed cluster service, so they do not match the Azure Synapse engines described.

HiveQL and Flink is incorrect because HiveQL is the query language for Apache Hive and Flink is a stream processing engine, and neither corresponds to the T-SQL and Spark pairing used in Azure Synapse.

T-SQL and Hadoop is incorrect because Hadoop refers to the older Hadoop ecosystem and MapReduce rather than the Apache Spark engine used by Databricks, even though T-SQL is a common abbreviation for Transact-SQL.

When answering engine pairing questions match the vendor specific names and watch for cloud mismatches. Pay attention to the names Transact-SQL and Spark and avoid choices that mention non Azure services like BigQuery.

The correct option is New data events are processed continuously as they arrive.

New data events are processed continuously as they arrive describes stream processing because events are handled in near real time as they occur and processing is ongoing rather than deferred. Stream processing enables low latency analytics, event time and windowing semantics, and stateful computations that operate on each incoming event or on short windows of events.

Several incoming records are buffered and then processed together in one operation is incorrect because that phrasing describes batching or micro batching where records are grouped and processed together instead of being processed continuously.

Cloud Dataflow is incorrect because it is a Google Cloud service that can implement both streaming and batch jobs but it is not the definition of stream processing itself. Cloud Dataflow is a tool you can use to do stream processing but the definition refers to how data is processed rather than which product is used.

Data is gathered into a temporary store and all records are processed together as a batch is incorrect because that option describes batch processing where data is collected and processed as a unit with higher latency compared to continuous stream processing.

When you see phrases like continuously or as they arrive think streaming. Look for clues about latency and whether records are grouped before processing to distinguish batch from stream.

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

To persist historical query performance metrics for trend analysis is the correct option.

The query store is designed to record query execution statistics and associated plans over time so database administrators and developers can analyze trends, identify regressions, and correlate performance changes with schema or workload shifts.

By keeping historical metrics the query store helps you compare past and present behavior, detect slowdowns, and choose or force stable execution plans when needed for troubleshooting and tuning.

Cloud SQL Insights is an interactive monitoring and diagnostics tool and not the query store feature. It is focused on surfacing recent query activity and diagnostics rather than acting as a long term persisted store of historical query metrics.

To schedule and manage automated database backups is incorrect because backups are about protecting and restoring data and schema and do not provide stored query performance metrics for trend analysis.

To cache frequently executed statements for faster response is incorrect because caching and plan caching are runtime optimizations and do not constitute a historical repository of query performance data for trend analysis.

When a question asks about a feature that stores information over time look for words like persist or historical because they usually point to a telemetry or store feature rather than backups or caching.

Azure Resource Manager templates is correct because those templates are declarative JSON declaration files that you author, store in version control, and treat as Infrastructure as Code.

Azure Resource Manager templates describe the desired state of Azure resources in JSON. You can parameterize them, include variables and outputs, and integrate them into CI CD pipelines so deployments are repeatable and auditable. That declarative JSON file model is the defining characteristic of ARM templates.

Google Cloud Deployment Manager is not correct because that service is Google Cloud’s equivalent templating system and it uses YAML, Jinja, or Python templates rather than Azure Resource Manager JSON templates. It applies to a different cloud platform and not to Azure JSON ARM templates.

PowerShell scripts are not correct because they are imperative scripts that execute commands to create or configure resources. You can store scripts in source control but they are not the same as a declarative JSON declaration file that is treated as Infrastructure as Code in the ARM template sense.

Azure Portal is not correct because it is an interactive web console for manual resource creation and management. The portal does not itself consist of an authored JSON declaration file that you check into version control and treat as IaC, even though the portal can sometimes export templates for later use.

When a question mentions a declarative JSON file stored in version control and treated as Infrastructure as Code choose Azure Resource Manager templates for Azure. Distinguish declarative templates from imperative scripts and from manual portal operations.

The correct options are A text box, A complete report page, and An individual visualization from a report.

A text box lets you add explanatory content directly on the dashboard so you can provide context titles and insights for viewers. It is a simple way to annotate data and guide interpretation without changing the underlying reports.

A complete report page can be embedded or referenced to present a full page of a report within the dashboard so you preserve the page layout and any interactive controls on that page. This is useful when you want to reuse a full report page as part of a larger dashboard experience.

An individual visualization from a report lets you place a single chart or table on the dashboard to highlight a specific metric or view. This lets you focus attention on one visualization while keeping other dashboard elements separate and focused.

A presentation slide is incorrect because a dashboard does not accept slide files as a native component and slides are not a built in Data Studio element. You can recreate slide like content with text boxes and charts but you cannot add a slide file as a single dashboard element.

When deciding which items can be added to a dashboard imagine the dashboard canvas and think about components you can insert. Use text boxes for context and add whole report pages or individual charts when you need interactive data.

The correct option is Columns.

Columns represent the attributes of a table and datatypes are assigned at the column level so the database can enforce the allowed type and format of values for that attribute. When you create or alter a table you declare each column with a name and a datatype such as integer varchar or date and that definition constrains the values stored in every row for that column.

Relationships describe how tables are linked through keys and associations and they do not define datatypes. A relationship indicates how rows in different tables relate to each other rather than specifying the type of a column.

Fields is an informal term that people sometimes use to mean a column or a cell but it is ambiguous. The question expects the precise relational term which is a column so Fields is not the best answer here.

Tables are the overall structures that contain rows and columns and you do not assign a single datatype to a table as a whole. Datatypes are specified on the individual columns inside the table.

Rows represent individual records or instances and they hold values for each column. Datatypes are not assigned to rows because each row is made up of columns that each have their own datatype.

When you read schema questions think about where attributes are defined and pick the answer that references columns or attributes in the table structure.

The correct answer is Azure Cosmos DB. It is the service that best fits a requirement to hold JSON records without a fixed schema while providing flexible indexing and querying capabilities.

Azure Cosmos DB is a globally distributed, multi model database that natively stores JSON documents and operates as a schema free document store. It automatically indexes properties by default so you can run rich, SQL like queries over JSON without needing to predefine secondary indexes. It also provides multiple APIs and consistency options along with predictable low latency and comprehensive SLAs, which makes it well suited to flexible document workloads that require powerful query capabilities.

Azure Blob Storage is object storage for large unstructured files and blobs. You can store JSON files in blobs but it does not provide automatic indexing or rich, ad hoc querying of JSON content without pairing it with additional services.

Azure SQL Database is a relational database that requires defined schemas for most workloads. It does include JSON functions for storing and querying JSON, but it is not optimized for schema free document workloads or for automatic indexing of arbitrary JSON properties in the same way a document database is.

Azure Table Storage is a simple key value NoSQL table store with limited querying capabilities. It does not offer the flexible document indexing or the rich query surface that you get from a document database such as Cosmos DB.

When you see requirements for schema free JSON and flexible indexing think document databases first and look for keywords like automatic indexing and rich query support to identify services such as Azure Cosmos DB.

The correct answer is To hold temporary tables table variables and intermediate result sets used while queries run.

In an Azure SQL Managed Instance tempdb is the workspace for transient objects and intermediate results that queries produce during execution. It stores temporary tables table variables and worktables that support operations such as sorting hashing and spooling. Because tempdb is recreated on service restart it is intended for short lived query processing state rather than for durable user data.

To record transactional history and durable log records is incorrect because transactional history and durable log records are written to a database’s transaction log for recovery and durability. Tempdb is not used for persistent logging and it is not part of a database recovery chain.

To maintain user account credentials and access control is incorrect because login information and security metadata are kept in the master database or managed through Azure AD and server level settings. Tempdb does not hold persistent security or account configuration.

To persist user created tables and stored procedures is incorrect because user objects that must persist are created in user databases. Tempdb can host temporary objects for a session but those objects are transient and are removed at session end or when the instance restarts.

When a question mentions temporary tables or intermediate results think tempdb and remember that it is transient and recreated at service restart so it does not persist user data.

The correct answer is Descriptive analytics.

Descriptive analytics summarizes past events using historical records and produces reports that show what happened without attempting to forecast outcomes or recommend actions. This category includes dashboards, aggregates, and routine reporting that provide insight into prior performance rather than predicting future trends or prescribing decisions.

Predictive analytics is incorrect because it uses statistical models and machine learning to forecast future outcomes and probabilities and it therefore goes beyond merely summarizing past records.

Prescriptive analytics is incorrect because it aims to recommend actions or policies using optimization or simulation rather than only summarizing historical events.

Diagnostic analytics is incorrect because it focuses on finding causes and explanations for past events and it often involves drilling into data to understand why something happened instead of just summarizing what happened.

Focus on keywords such as summarizes and historical to identify descriptive scenarios and eliminate options that imply prediction or recommendation.

The correct option is An identity object that represents a user group service account or managed identity that is requesting access to cloud resources.

An identity object that represents a user group service account or managed identity that is requesting access to cloud resources is the definition of a security principal because the principal is the entity that makes a request and is evaluated for access. The cloud platform checks who the principal is and then evaluates roles and policy bindings to determine whether the principal has the necessary permissions to perform the requested action.

A named set of permissions that can be assigned to identities such as roles like Owner or Editor is incorrect because that describes a role, which is a bundle of permissions granted to principals and not the principal itself.

A collection of resources grouped under a project to which access controls can be applied is incorrect because that describes a project or resource grouping, which is a scope for resources and policies and not an identity that requests access.

A policy binding that attaches a role to a specific member within an IAM policy is incorrect because that describes a binding, which ties a role to a principal, and not the principal itself.

When a question asks about a security principal look for answers that describe an identity such as a user group service account or managed identity rather than roles projects or bindings.

The correct answer is Consistent Prefix.

Consistent Prefix guarantees that reads will return a prefix of the global sequence of writes so updates are observed in the same order they were applied even when there is a delay before those updates become visible. This consistency level allows clients to observe stale data during the delay while still preserving the original write order across replicas.

Bounded Staleness is incorrect because it bounds how far reads can lag behind writes by a count or a time window rather than simply guaranteeing ordered visibility with unrestricted delay. The question emphasizes ordered visibility with possible staleness rather than a fixed staleness bound.

Session is incorrect because it provides consistency guarantees scoped to a single client session such as read your writes and monotonic reads, and it does not ensure the global ordering of updates across all clients that consistent prefix provides.

Strong is incorrect because it enforces linearizability so clients do not see stale data and updates appear immediately. The scenario in the question allows stale reads, so strong consistency does not match.

Eventual is incorrect because it gives no ordering guarantees and only ensures that replicas will converge eventually. Reads under eventual consistency can observe out of order updates, so it does not satisfy the ordered visibility requirement.

When a question mentions that updates must be seen in order but that stale reads are allowed, pick Consistent Prefix since it preserves order while permitting delays.

Batch processing is correct because it describes processing multiple records together as a collection rather than handling each record one by one.

Batch processing groups records into a single job or batch and then processes that set as a unit. This approach is common for offline analytics and ETL jobs where high throughput is more important than low latency.

Stream processing is incorrect because it focuses on processing events continuously and often one record or a small window of records at a time to enable near real time results.

Buffering is incorrect because buffering is simply a temporary holding of data while it waits for processing and it does not by itself define whether records are processed in batches or individually.

Template rendering is incorrect because it relates to generating formatted output from a template and data and it is not a data processing mode for handling multiple records as a collection.

When a question contrasts handling records together versus one by one look for the words batch and stream and think about whether the use case values throughput over latency.

The correct option is Load data into Power BI Desktop, design reports, publish to the Power BI service and then view and interact in the service and mobile.

This answer is correct because Power BI Desktop is the primary authoring environment where you import, shape, and model data and create visuals. After authoring you publish the report to the Power BI service to enable sharing, scheduled refresh, and collaboration, and then users view and interact with the published report in the service or on mobile devices.

Create a report in the Power BI service then send it to the Power BI mobile app and open it in Power BI Desktop is incorrect because the typical flow is to author in Power BI Desktop rather than author in the service and then reopen in Desktop. The mobile app is intended for viewing and interacting and not for authoring or transferring reports back to Desktop.

Import data into Power BI mobile, build the report there and then export it to Power BI Desktop is incorrect because the Power BI mobile apps do not support full data import and report authoring or exporting to Desktop. Mobile apps are designed for consuming published content.

Author paginated reports in Power BI Report Builder then publish them and try to edit the original dataset in Power BI Desktop is incorrect because paginated reports are a separate scenario created with Report Builder for pixel perfect output. You do not usually publish a paginated report and then edit its dataset in Desktop as datasets are often managed in the service or created in Desktop first.

When answering workflow questions identify which tool is for authoring and which tools are for sharing and consumption. Remember that Power BI Desktop is the authoring tool and the Power BI service and mobile apps are for publishing and viewing.

The correct answer is Azure Automation.

Azure Automation provides centralized runbooks that you can author in PowerShell or Python and you can schedule and manage them across many databases to perform maintenance jobs such as backups, rebuilding indexes and updating statistics. It also supports hybrid runbook workers so you can run maintenance against on premises resources and it integrates with Azure RBAC and the Azure Resource Manager APIs to authenticate and orchestrate tasks across your environment.

Azure Logic Apps is designed for workflow integration and connecting services with many built in connectors. It can run scheduled workflows but it is not focused on runbook authoring and enterprise runbook management the way Azure Automation is.

Azure Event Grid is an event routing service that delivers events to subscribers. It is not a scheduling or runbook orchestration platform and it cannot manage centralized maintenance runbooks across databases.

Azure Functions offers serverless compute and you can trigger code on timers or events. It can perform automated tasks but it lacks the built in runbook management features, scheduling and hybrid worker model that Azure Automation provides for enterprise maintenance across many databases.

When a question mentions creating, scheduling and managing runbooks look for services that explicitly provide runbook and schedule features and support hybrid runbook workers for on premises scenarios.

The correct option is The data is stored raw and structure is applied only when the data is read.

The data is stored raw and structure is applied only when the data is read means that a data lake keeps the original files as they arrive and does not impose a predefined schema when data is ingested. The schema or structure is applied at query or processing time so different formats and evolving record shapes can be handled without rejecting data at ingest.

Data is encrypted on arrival to ensure confidentiality is incorrect because encryption is a security control and it does not describe when or how schema is applied. An encrypted file can be used with either schema on read or schema on write.

A strict schema is enforced at ingest which rejects records that do not match is incorrect because that describes schema on write. Schema on write requires a predefined schema at ingest and can reject or transform records that do not conform.

Metadata and a schema are automatically derived and stored when the data is loaded is incorrect because that implies the system determines and persists a schema at load time. Some systems can auto-detect schema and store metadata but that is different from schema on read which delays applying structure until the data is actually read.

When answering data lake questions focus on when the schema is applied. Remember that schema on read means schema is applied at query time and schema on write means schema is applied at ingest.

The correct answer is Core SQL API.

Core SQL API is designed to store and query JSON documents and it exposes a SQL like query language that you can use to filter, project, and aggregate JSON data. It supports automatic indexing of JSON properties and familiar SQL style syntax so you can run SELECT FROM WHERE and JOIN queries across document collections.

MongoDB API is for applications that use the MongoDB wire protocol and the Mongo query language. It stores BSON or JSON like documents but you would use Mongo query operators rather than the SQL like language provided by the SQL API.

Gremlin graph API is intended for graph data models and traversals. It uses the Gremlin traversal language and is optimized for nodes and edges so it is not appropriate for SQL style queries over JSON documents.

Cassandra API implements a wide column model compatible with Cassandra and uses CQL. It is focused on table like and wide column data rather than document oriented SQL queries, so it is not the right choice for JSON document queries.

When a question asks about storing JSON documents and running SQL style queries choose Core SQL API and focus on matching the API to the data model mentioned in the question.

The correct answer is Big data.

Big data refers to datasets that are too large or complex for conventional database systems and systems that handle Big data must support fast data ingestion and processing and they must provide storage for results and enough compute to run analytics over those results.

Apache Beam is a programming model and set of SDKs and runners for defining data processing pipelines and it describes how to process data rather than the description of the dataset itself.

Google BigQuery is a managed data warehouse that you can use to analyze large datasets so it is a tool for working with big data rather than the general term that describes the datasets.

Ceph is a distributed storage system used to store objects and blocks and file data and it is not the conceptual term for large or complex datasets.

Hadoop is a framework and ecosystem for distributed storage and processing of large data so it is a technology for implementing big data solutions rather than the name of the datasets themselves.

Cloud Pub/Sub is a messaging and ingestion service used to move data between systems and it is a component you can use in big data architectures rather than the definition of big data.

When a question describes characteristics of datasets and processing needs pick the broader concept answer such as Big data instead of specific products or frameworks.

The correct option is SQL Server Management Studio (SSMS) is a graphical management console for administering SQL Server and Azure SQL instances and it supports configuration management and administrative tasks Azure Data Studio is a lightweight cross platform editor for running ad hoc queries and saving results as text JSON or spreadsheet formats.

SQL Server Management Studio (SSMS) is the full featured graphical tool used to perform configuration, security, backup, and other administrative tasks for SQL Server and Azure SQL instances. It provides server and instance management capabilities that are suited to database administrators.

Azure Data Studio is a lightweight, cross platform editor that focuses on ad hoc querying, notebooks, and modern result export options such as text, JSON, and spreadsheet formats. It is designed for developers and data professionals who need a fast, cross platform query experience rather than full server administration.

Azure Data Studio is a graphical administrative console for server configuration and instance management and SQL Server Management Studio (SSMS) is a lightweight cross platform editor for executing on demand queries and exporting results is incorrect because it swaps the primary roles of the two tools. SSMS is the administrative console and Azure Data Studio is the lightweight cross platform editor.

SQL Server Management Studio (SSMS) is a development suite for authoring Analysis Integration and Reporting Services projects and Azure Data Studio is a full fledged IDE for building complete database solutions is incorrect because SSMS is primarily an administration and management tool even though it can interact with some development projects and Visual Studio is typically used for full service development workflows. Azure Data Studio is not a full fledged IDE for building complete database solutions and it focuses on querying and notebooks rather than comprehensive project authoring.

Azure Data Studio is a Microsoft extension that enables direct querying of BigQuery datasets and SQL Server Management Studio (SSMS) is a Cloud Console plugin for managing Cloud SQL instances is incorrect because BigQuery and Cloud SQL are Google Cloud services and those descriptions do not reflect the native purposes of the Microsoft tools. Azure Data Studio does not natively act as a BigQuery extension and SSMS is not a Cloud Console plugin for Cloud SQL.

Focus on keywords that describe administration versus ad hoc querying and match them to the tool roles. Look for mentions of configuration management for admin tools and cross platform editor for lightweight query tools.

Batch processing is the correct option because it specifically means collecting records and processing them together in grouped units.

Batch processing accumulates raw inputs over a period of time and runs ordered operations on the accumulated set as a single job or set of jobs. This approach is chosen when throughput and efficient bulk computation are more important than low latency and when you can tolerate processing delays while you wait to gather a group of records.

Data ingestion refers to the general act of bringing data into a platform and can include both streaming and batch arrival modes, so it does not specifically mean processing records in grouped units. It is a broader term that describes input rather than the processing pattern.

Cloud Pub/Sub is a messaging and event delivery service that is commonly used for streaming ingestion and decoupling producers from consumers, but it is not the name of the grouped processing pattern. It supports streaming pipelines rather than defining batch grouping.

Streaming is the opposite pattern where records are handled as they arrive to provide low latency processing. Since the question asks about accumulating records and processing them together, streaming is not the correct choice.

Look for words like accumulate, group, or process together in the question as clues that the answer is about batch processing rather than streaming or general ingestion.

The correct answer is Vertex AI.

Vertex AI provides managed online prediction endpoints that let you deploy models as low latency APIs for instantaneous predictions. It handles autoscaling, model versioning, traffic splitting, monitoring, and built in explainability so teams do not need to manage serving infrastructure.

Vertex AI integrates with AutoML and custom model containers and offers a purpose built experience for machine learning serving which matches the requirement for serverless scalable APIs for model inference.

Cloud Run is a serverless container platform that can host model containers however it does not provide the ML serving features that Vertex AI offers such as built in model monitoring, versioning, and explainability, so it is not the best managed ML serving choice.

Google Kubernetes Engine gives full control over clusters and workloads but it requires more operational management and it is not a serverless managed ML service, so it does not match the question intent.

Cloud Functions is an event driven serverless compute option with execution time limits and cold start behavior and it lacks the model serving features of Vertex AI so it is not suitable for high performance online model APIs.

Look for services that explicitly mention managed online prediction endpoints, autoscaling, and model monitoring when the question asks for serverless ML serving. Vertex AI is the Google Cloud service purpose built for production model inference while the others are general compute options.

The correct answer is Azure Cosmos DB.

Azure Cosmos DB is a globally distributed, multi model database that natively stores JSON documents and delivers reads and writes in milliseconds. It provides automatic indexing and query capabilities so you can run aggregations and produce analytical reports without building separate ETL pipelines.

Azure Cosmos DB also supports autoscale throughput and integrates with Azure Synapse Link to enable near real time analytics over operational JSON data without extra development effort. Those features meet the question requirements for low latency, high availability, automatic scaling and built in analytics.

Azure SQL Database is a relational database and while it can store JSON text it is not a native, globally distributed document store and it generally requires more schema design and ETL work to support large scale JSON aggregations.

Azure Data Lake Storage is an object and file storage service optimized for large scale batch analytics and data lakes and it requires processing engines or pipelines to aggregate JSON documents. It does not provide the low latency operational query surface that a document database provides by itself.

Azure Blob Storage is an unstructured object store and it does not offer native, indexable JSON document queries or automatic global distribution and autoscale for low latency reads. Aggregating JSON stored in blobs needs additional services and development to achieve the scenario described.

When a question asks for native JSON storage with real time analytics and minimal development look for a managed document database and integration with analytics connectors such as Azure Cosmos DB.

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

The table enforces that each primary key value is unique so no two rows share the same key.

A primary key is a table constraint that uniquely identifies each row and it prevents duplicate values for the key columns. Database systems enforce this by creating a unique constraint or index and they typically also require primary key columns to be non nullable. The uniqueness requirement is what makes the primary key suitable for referencing rows from other tables and for maintaining referential integrity.

Every table is required to declare exactly one primary key is incorrect because SQL does not force every table to have a primary key. Many tables are created without a primary key and it is valid to design tables without one when appropriate.

A relational table can hold multiple primary keys is incorrect because a table may have only a single primary key constraint. You can define multiple unique constraints but those are not primary keys. A primary key can span multiple columns when composite keys are used but it is still a single primary key.

Defining a primary key forces all rows in a partition to share the same key value is incorrect because a primary key enforces uniqueness not sameness. Partitioning and primary keys are separate concepts. The partition key or a clustering column may determine how rows are grouped but they do not change the uniqueness requirement of the primary key.

Focus on the words unique and identify when the question asks about primary keys and watch for answers that confuse primary keys with partitioning or optional design choices.

The correct answer is Improves analytical query speed by reading only the columns that are needed.

Columnar storage organizes data by column so queries only need to scan the specific columns they reference. This reduces the amount of data read from disk and lowers I/O overhead which makes analytical queries that aggregate or filter on a subset of columns much faster.

Reduces storage costs through higher compression rates is not the best choice because higher compression can be a side benefit of columnar formats but the primary performance gain for analytics comes from reading fewer columns rather than from storage cost savings.

Provides stronger controls for protecting sensitive fields is incorrect because access control and data protection are provided by the database or platform features rather than by the columnar layout itself. Column organization by itself does not enforce stronger security controls.

Increases the speed of ingesting data from many sources is wrong because columnar formats often favor read and compression efficiency and they may require batch or transformation steps during ingestion. Ingest performance depends more on the ingestion pipeline and tooling than on using a columnar format.

When you see questions about columnar storage focus on whether the option describes reduced read work. Reading fewer columns is the key advantage for analytical query performance.

The correct answer is Azure Analysis Services.

Azure Analysis Services provides a managed semantic modeling engine that lets analysts create tabular models for OLAP style queries by combining data from multiple sources and embedding business rules and measures. It supports tabular modeling and query capabilities that are intended for analytical workloads and it acts as the reusable analytical layer that BI tools query.

Azure Synapse Analytics is a unified analytics platform for data integration, warehousing and big data processing but it does not offer a dedicated managed tabular model semantic layer like Analysis Services.

Azure Cosmos DB is a globally distributed NoSQL database designed for transactional and operational workloads and it is not intended to host OLAP tabular models or semantic business logic.

Azure SQL Database is a managed relational database that can store and serve data for analytics but it does not provide the specialized tabular modeling and semantic layer features needed to build OLAP tabular models.

Azure Data Lake Storage is scalable storage for big data files and it is used to store raw and processed datasets rather than to create tabular models or embed business rules for OLAP querying.

When a question asks about building a semantic model or tabular model look for services that explicitly provide an analytical semantic layer rather than storage or transactional databases.

The correct option is Platform as a service managed offering.

NebulaDB hosted by Stratus Cloud is provided as a managed database platform where the provider handles the underlying infrastructure, the database engine, backups, updates, and scaling so customers can focus on their applications and data rather than system administration.

Because the vendor operates the runtime and manages operational tasks it matches the platform as a service managed model rather than being a simple virtual machine layer or a complete end user application.

Software as a service is incorrect because SaaS delivers complete applications to end users and does not describe a managed database platform that customers integrate into their own applications.

Infrastructure as a service is incorrect because IaaS supplies raw compute, storage, and networking for customers to configure and operate software themselves, whereas NebulaDB is offered as a managed platform with the provider running the database service.

When a question describes the provider handling maintenance, scaling, or backups look for PaaS or a managed platform as the likely answer.

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

The correct option is Key constraints.

Key constraints are the mechanism that relational databases use to enforce relationships between tables. Primary keys uniquely identify rows in one table and foreign keys reference those primary keys in another table so the database can maintain referential integrity and prevent orphaned or inconsistent records. Databases also use key constraints to enforce rules such as cascade updates and deletes when related rows change.

Table partitions are about dividing a large table into smaller pieces for performance and maintenance and they do not enforce relationships between tables. Partitioning helps with query performance and data management but it does not provide referential integrity.

Columns are the fields that store data and they define the structure of a table. Columns by themselves do not enforce relationships unless they are part of declared key constraints such as primary keys or foreign keys.

Indexes improve query performance by speeding up lookups and joins but they do not by themselves maintain or enforce the logical relationships between tables. Indexes can support the performance of key lookups but referential integrity is provided by key constraints.

When you see questions about enforcing relationships think of primary keys and foreign keys rather than performance features like partitions or indexes.

Azure Data Factory is the correct option.

Azure Data Factory is Azure’s managed data integration service that lets you create data driven workflows to orchestrate data movement and to transform data at scale. It enables composing and scheduling pipelines that ingest from multiple data sources and authoring visual data flows for complex ETL tasks which matches the sentence description.

Azure PowerShell provides command line cmdlets for automating and managing Azure resources and it is not a managed service for building and scheduling data integration pipelines.

Cloud Data Fusion is a managed data integration service but it is part of Google Cloud and not the Azure service described in the question.

Azure Portal is the web based management console for Azure and it is used to manage resources and monitor services rather than to author and orchestrate large scale ETL pipelines as a managed data integration product.

When a question mentions managed data integration, pipelines, or visual data flows look for the service that explicitly names ETL and orchestration. On Azure focused questions that phrasing usually indicates Azure Data Factory.

The correct answer is: Mark the end of a batch of statements.

The Mark the end of a batch of statements option is correct because GO is a batch separator that client tools such as sqlcmd and SQL Server Management Studio recognize to indicate that the current set of Transact SQL statements should be sent to the server together. It is not a Transact SQL statement executed by the server and it simply tells the client to submit the current batch for execution.

The Mark the end of a batch of statements behavior also affects scope and statement ordering. Variables declared in one batch are not visible in the next batch and some statements must appear at the start of a batch so splitting with GO is often required in scripts.

Invoke a stored procedure to execute its logic is incorrect because stored procedures are invoked with EXEC or EXECUTE and not with GO. GO does not call or run a procedure.

Start a transaction that spans multiple commands is incorrect because transactions are started with BEGIN TRANSACTION and controlled with COMMIT or ROLLBACK. GO does not begin or commit a transaction and it is only a batch separator on the client side.

Convert several lines into a commented out block is incorrect because commenting is done with — for single line comments or with /* and */ for block comments. GO does not turn lines into comments.

When a question refers to script structure or batch boundaries watch for the word batch and remember that GO is a client side batch separator and not a Transact SQL command.

The correct option is Contoso Blob Storage.

Contoso Blob Storage is the general purpose object storage service that is designed for large, unstructured binary files and fits a wide range of scenarios. It stores data as objects or blobs rather than as blocks or files, and it provides scalable, durable, and cost effective storage for items such as images, videos, backups, and logs.

Contoso Queue Storage is incorrect because queue storage is meant for messaging and decoupling application components and not for storing large binary objects. It handles small messages rather than large files.

Contoso Disk Storage is incorrect because disk storage provides block storage that is attached to virtual machines for operating systems and applications and it is not intended as general purpose object storage for large unstructured files.

Contoso File Storage is incorrect because file storage offers managed file shares that provide file system semantics over SMB or NFS and it is optimized for shared file access rather than for object storage of large binary blobs.

When a definition mentions large, unstructured binary objects think blob or object storage and eliminate services aimed at messaging, block disks, or file shares.

CREATE TABLE is the correct option.

The CREATE TABLE statement is a Data Definition Language command used to define new tables including their columns types and constraints. It changes the database schema by creating database objects and it is therefore considered part of DDL rather than a query or row level operation.

MERGE is not correct because MERGE is used to insert update or delete rows by merging data from a source into a target and it operates on row data rather than on the schema.

JOIN is not correct because JOIN is a clause used inside queries to combine rows from multiple tables and it is not a standalone DDL statement that creates or modifies schema objects.

SELECT is not correct because SELECT retrieves data from the database and does not define or alter tables or other schema objects.

When you see a question about schema creation think DDL and look for verbs like CREATE ALTER or DROP which define or change structure rather than verbs that read or manipulate rows.

Relational database table and Delimited spreadsheet file are correct.

A Relational database table organizes data into rows and columns with a fixed schema and defined data types for each field. This fixed schema and tabular layout make it easy to query the data with SQL and enforce constraints so it is a canonical example of structured data.

A Delimited spreadsheet file such as a CSV uses a consistent column layout and delimiters so each column maps to a specific field across rows. When the columns are well defined it can be loaded into databases and treated as structured data.

An Cloud Storage object is simply a file stored in object storage and it does not imply any internal schema. Objects in Cloud Storage can contain structured, semi structured, or unstructured data so the storage object itself is not inherently structured.

A JSON document is typically considered semi structured because it allows nested structures and varying fields across records. That flexibility is useful, but it means JSON does not have the fixed tabular schema that defines structured data in the classical sense.

When evaluating examples look for a fixed schema with consistent rows and columns. If the data layout is predictable and each field has a defined type it is likely structured.

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

Automated backups provided by the managed instance is correct.

The managed instance includes built in automated backups that capture full, differential, and transaction log backups so you can perform point in time restores and configure long term retention without creating custom jobs. Using the automated backup capability reduces operational overhead and it is the simplest way to ensure backups for a mid sized retailer like Orion Retail.

Create a scheduled SQL Agent job to perform database backups is not the simplest option because it requires you to build and maintain custom jobs and schedules when the managed service already performs automated backups.

Manually export database backups to an Azure Storage account is possible but it is labor intensive and error prone for regular backups. Manual exports do not provide the automated point in time restore experience that the managed instance backup system gives you.

Azure Backup is a separate service and it is not required for the managed instance automated backup scenario. Azure Backup adds extra configuration and complexity when the managed instance natively provides automated backups and retention controls.

When a question asks for the simplest or least operational approach look for answers that describe built in managed service features. Managed instance automated backups are usually the intended choice.

The correct option is Availability Zones.

Availability Zones are distinct fault domains inside a cloud region and they are used to place replicas in separate physical locations to reduce the impact of a single zone failure and to improve overall availability. When provisioning a NebulaDB account the capability to select availability zones is what ensures replicas are distributed across multiple zones within the same region so that an outage in one zone will not take down all replicas.

Multi-master replication refers to a replication topology that allows multiple nodes to accept writes and it addresses write availability and conflict handling. It does not by itself specify how replicas are placed across physical zones so it is not the correct choice.

Regional Persistent Disk is a block storage feature in some cloud providers that replicates data across zones for durability and failover. It is a storage level feature and not the database provisioning capability that controls where NebulaDB places its replicas, so it is not the correct answer.

Strong consistency model describes the guarantees about read and write ordering across replicas and it affects correctness of data access. It does not determine the physical distribution of replicas across availability zones and therefore it is not the right option.

When a question asks about distributing replicas across the cloud look for terms that describe physical placement such as Availability Zones instead of terms that describe consistency or replication modes.

The correct answer is Partition key and row key.

Partition key and row key together form the composite primary key for an entity in table storage. The partition key groups related entities and helps distribute data across storage nodes while the row key uniquely identifies an entity within that partition. Together they allow the service to locate an entity efficiently by first narrowing to a partition and then finding the row inside it.

Project ID and table ID is incorrect because table services do not use a project identifier and table identifier as a composite lookup key to identify a single entity. The project concept applies to other platforms and it does not provide the per entity uniqueness that the partition and row keys provide.

Private key and public key is incorrect because those terms refer to cryptographic keys used for authentication and encryption and they are not used as storage keys to locate entities in a table.

Table name and column name is incorrect because the table name only identifies the table and a column name is a schema attribute. A column name does not uniquely identify an entity and it is not used as the composite storage key for entity lookup.

When you see a question about how a specific row or entity is located think of the table service composite key and remember the terms partition key and row key.

Azure Data Factory is the correct choice for ingesting data from multiple on premises systems and cloud sources into Azure Synapse Analytics.

Azure Data Factory is a fully managed data integration service that includes built in connectors for databases, files, message stores and cloud services and it provides the Copy Activity and pipelines to move and transform data at scale into Synapse.

Azure Data Factory supports a self hosted integration runtime to securely connect to on premises systems and it can orchestrate scheduled and event driven pipelines so teams can centralize ingestion into Azure Synapse Analytics.

Azure Event Hubs is a high throughput streaming platform and event ingestion service and it is intended for telemetry and real time event streams rather than for orchestrated, connector based bulk data movement into Synapse.

Power BI is a business intelligence and reporting tool for visualization and analysis and it does not provide the pipeline orchestration and broad source connectivity needed to perform enterprise data ingestion into Synapse.

Azure Databricks is an analytics and data processing platform that is excellent for transformations and machine learning and it can read and write data to Synapse but it is not primarily a managed data integration service for connecting many on premises systems and orchestrating bulk loads.

When a question asks which service to use for ingesting data into Synapse look for a managed data integration service that mentions connectors and an integration runtime. Consider Azure Data Factory for data movement and orchestration.

The correct answer is It is the practice of replacing or obscuring sensitive fields so they remain usable for testing and analytics.

Data masking means replacing or obscuring sensitive values so that the data retains structure and usefulness for testing and analytics while reducing the risk of exposing real personal information. Masking approaches include substitution, tokenization, redaction, and format preserving transformations and they are chosen to balance data utility and privacy for nonproduction use cases.

Cloud DLP is a Google Cloud service that can discover, classify, and deidentify sensitive data and it can perform masking operations, but naming a product does not define the general concept of data masking itself. The product is an implementation and not the definition.

A technique for reducing dataset size to lower storage costs describes compression, sampling, or archival strategies and it does not capture the idea of obscuring sensitive fields while keeping data usable for testing and analytics.

A method of encrypting confidential information to protect privacy refers to cryptographic protection that transforms data into ciphertext and is typically reversible with keys. Encryption protects data confidentiality but is different from masking because masking intentionally changes or hides values to enable safe use in nonproduction contexts.

Focus on whether the option emphasizes keeping data usable for testing and analytics to identify masking. Look for words like obscuring or deidentification rather than encryption or compression

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

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The correct option is Range index.

Range index is appropriate because it supports efficient filtering and sorting over intervals for numeric and string values, which is what you need for time based queries on the recorded_time attribute. In Azure Cosmos DB a range index enables range scans and order by operations so queries that filter or sort by time will perform better when that index is present.

Spatial index is incorrect because spatial indexes are intended for geospatial queries such as distance calculations and polygon containment and they do not optimize range scans or ordering for a timestamp field.

Hash index is incorrect because hash indexes are optimized for exact match or equality lookups and they do not support efficient range queries or sorting that are needed for time interval filters.

Unique index is incorrect because unique indexes enforce value uniqueness across documents and they are not designed to improve performance for range filtering or sorting by time. Use a unique index only when you must guarantee that no two documents share the same value for an attribute.

When a question mentions filtering or sorting across intervals think about indexes that support range scans. Remember that hash is for exact matches and spatial is for geospatial queries.

False is correct because the statement asserts an absolute requirement that does not hold for most modern migration workflows.

Many database migration utilities use change data capture and continuous replication so you can keep the target in sync while the source remains writable. This approach lets you perform a short final switchover when replication is nearly caught up rather than requiring a full suspension of all on-site writes for an extended period. The need for a complete write freeze depends on the chosen migration method and tool rather than being an inherent rule.

The claim that a single utility always restores a backup directly into instances on a given cloud provider is also too broad. Some migrations perform an initial bulk load from a backup and then replicate changes, while others import backups into intermediary storage before provisioning databases. Tool capabilities and provider requirements determine the exact process.

True is incorrect because it treats the suspension of all on-site write activity as a universal requirement and that absolute wording makes the statement false for many real world migration scenarios.

When you encounter absolute wording such as must or always in migration questions remember that techniques like continuous replication or change data capture often avoid the need for long write suspensions.

The correct answer is Analytical.

An Analytical system is designed to help business stakeholders run queries and obtain a strategic view of the data stored in a database and it focuses on collecting raw information and turning it into insights that inform business decisions. These systems are commonly implemented as data warehouses or OLAP platforms and they prioritize complex queries, aggregation, and historical analysis rather than high volume transactional processing.

Qualitative is incorrect because that term refers to types of data or methods of analysis that emphasize non numeric observations and it is not the standard label for a system built for strategic queries and analytics.

Operational is incorrect because operational systems support day to day business processes and real time operations and they are not primarily focused on the aggregated, strategic analysis described in the sentence.

Transactional is incorrect because transactional systems are optimized for processing individual business transactions and maintaining data integrity for operational workloads and they are not intended for the broad analytical queries used to inform high level business decisions.

When a question uses words like strategic, queries, aggregate, or historical prefer an analytical answer. When it mentions transactions, real time, or operational prefer a transactional or operational system.

The correct answer is False.

You can validate and filter telemetry and batch records before they are persisted by using features in the ingestion layer and streaming pipelines. Cloud Pub/Sub supports subscription filters and client side validation and Cloud Dataflow or Apache Beam pipelines can apply transforms to drop malformed records prior to writing to storage.

Dataflow and Beam also support stateful processing and deduplication patterns so duplicates can be removed in flight by using message identifiers or keys and then only clean data is written to BigQuery or Cloud Storage. BigQuery streaming inserts can also leverage insertId to help avoid duplicates when configured correctly.

True is incorrect because it claims that records cannot be filtered until after persistence. In real architectures you can and often should filter and deduplicate during ingestion or in-stream so that corrupted or duplicate records never get persisted.

When you see pipeline questions think about where processing can happen and remember that validation and deduplication can run before the data is written to storage.

A retail analytics team at Meridian Retail is creating a Cosmos DB container to capture product reviews during sustained heavy ingest. Which partition key approach will most likely balance write traffic evenly across partitions?

• ❏ A. Partition by the review submission timestamp

• ❏ B. Cloud Bigtable

• ❏ C. Use a per item random hash as the partition key

• ❏ D. Partition by product SKU

Which job role at Meridian Analytics is responsible for designing testing and maintaining databases and data structures and for aligning the data architecture with business objectives and for acquiring data and developing processes to create and retrieve information from datasets while using programming languages and tools to analyze the data?

• ❏ A. Machine Learning Engineer

• ❏ B. Database Administrator

• ❏ C. Data Analyst

• ❏ D. Data Engineer

A regional software vendor must retain backups for recovery and maintain long term archives of large unstructured files. Which Azure storage offering is most suitable for storing backup copies and archival data?

• ❏ A. Azure Disk Storage

• ❏ B. Azure Blob Storage

• ❏ C. Azure Cool Storage Tier

• ❏ D. Azure Files Storage

Which access control mechanism is active by default for every Azure SQL database?

• ❏ A. Azure Firewall

• ❏ B. Network security group

• ❏ C. Cloud Armor

• ❏ D. Server-level firewall

A payments fraud team at Clearwater Retail wants to deploy a machine learning pipeline that evaluates transaction streams in real time for fraudulent activity. Which Azure service is most appropriate for applying machine learning models to streaming data for immediate fraud detection?

• ❏ A. Cloud Dataflow

• ❏ B. Azure Machine Learning

• ❏ C. Azure Stream Analytics

• ❏ D. Azure Databricks

A regional analytics group needs a table style storage solution that has these benefits It takes the same time to insert a row into an empty table or a table that already has billions of records A storage container can hold up to 250 TB of data Tables can store semi structured entries There is no need to manage complex relational mappings Row writes are fast and reads are efficient when queries use the partition and row keys Which storage type best fits this description?

• ❏ A. Google Cloud Spanner

• ❏ B. Cloud SQL for PostgreSQL

• ❏ C. Pioneer Table Storage

• ❏ D. Google Cloud Bigtable

• ❏ E. Cloud SQL for MySQL

In the context of NovaCloud data services identify the missing term in this sentence. Apart from authentication and authorization many services offer additional protection through what feature?

• ❏ A. Cloud Key Management Service

• ❏ B. Advanced Data Security

• ❏ C. VPC Service Controls

• ❏ D. Cloud SQL Managed Instance

Which tool in Contoso Cloud provides a graphical interface that allows you to run queries, perform common database administration tasks, and produce scripts to automate database maintenance and support operations?

• ❏ A. Azure Synapse Studio

• ❏ B. Azure Data Studio

• ❏ C. SQL Server Management Studio

• ❏ D. Azure Portal Query Editor

Organizations often blend traditional data warehousing for business intelligence with big data techniques to support analytics. Traditional warehouses usually copy records from transactional systems into a relational database that uses a schema tuned for querying and building multidimensional models. Big data solutions handle very large datasets in varied formats and they are loaded in batches or captured as real time streams into a data lake where distributed engines like Apache Spark perform processing. Which Azure services can be used to build a pipeline for ingesting and processing this data? (Choose 2)

• ❏ A. Azure SQL Database

• ❏ B. Azure Synapse Analytics

• ❏ C. Azure Databricks

• ❏ D. Azure Stream Analytics

• ❏ E. Azure Data Factory

• ❏ F. Azure Cosmos DB

On the Contoso Cloud platform which term best completes this sentence Databases of this category generally fall into four types namely key value stores document databases column family databases and graph databases?

• ❏ A. SQL

• ❏ B. Cloud Bigtable

• ❏ C. JSON

• ❏ D. NoSQL

Fill in the missing terms in this sentence about Orion Cloud object storage. Object storage offers three access tiers that help balance access latency and storage cost [A], [B], and [C]?

• ❏ A. Nearline tier, Coldline tier, Archive tier

• ❏ B. Static tier, Fluid tier, Hybrid tier

• ❏ C. Hot tier, Cool tier, Archive tier

• ❏ D. Hot tier, Warm tier, Cold tier

Many managed database offerings remove most of the operational overhead and include enterprise features such as built-in high availability, automatic tuning, and continuous threat monitoring while providing strong uptime guarantees and global scalability. Which Microsoft Azure SQL engine is intended for Internet of Things scenarios that must process streaming time series data?

• ❏ A. Azure SQL Database

• ❏ B. SQL Server on Azure Virtual Machines

• ❏ C. Azure SQL Edge

• ❏ D. Azure SQL Managed Instance

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

A logistics firm named Meridian Labs uses Contoso Cloud to gather telemetry from thousands of connected trackers and needs a service that can ingest high velocity streams and perform immediate analysis of the incoming data what service should they choose?

• ❏ A. Contoso Data Factory

• ❏ B. Contoso Machine Learning

• ❏ C. Contoso Stream Analytics

• ❏ D. Contoso Event Hubs

Match each data integration component to the correct description and choose the grouping that aligns with their definitions?

• ❏ A. Dataset is a model of how data is organized inside storage. Mapping data flow is the connection information used to reach external systems. Pipeline is a scheduled grouping of tasks

• ❏ B. Dataset represents the data structure stored in a source or sink. Linked service holds the connection details and credentials needed to access external systems. Pipeline is a logical collection of activities that execute together and can be scheduled

• ❏ C. Linked service models the shape of data in stores. Mapping data flow performs visual data transformations at scale. Pipeline is a logical grouping of activities

• ❏ D. Dataset represents the data layout. Linked service stores connection details. Mapping data flow refers to Google Cloud Dataflow rather than transformation definitions. Pipeline is a grouping of activities

Fill in the missing word in the following description used by Contoso Cloud. A(n) [?] document is enclosed in curly brackets { and } and each field has a name which is then separated from its value by the word colon. Fields can hold simple values or nested objects and arrays are enclosed by square brackets. String literals are wrapped in “quotes” and fields are separated by commas. What is the missing word?

• ❏ A. HTML

• ❏ B. YAML

• ❏ C. XML

• ❏ D. JSON

Which Microsoft Azure service is intended for low latency processing of continuous event streams and provides built in machine learning scoring capabilities?

• ❏ A. Azure Synapse Analytics

• ❏ B. Azure Databricks

• ❏ C. Azure Stream Analytics

• ❏ D. Azure Machine Learning

In a relational database tables represent collections of real world entities and each row stores one instance of an entity. For example a digital bookstore might use tables named patrons titles purchases and purchase_items to record customer and order information. Which two key types are required to model a one to many relationship between two tables? (Choose 2)

• ❏ A. Index

• ❏ B. Foreign key

• ❏ C. Unique constraint

• ❏ D. Primary key

A small retailer named MapleMarket is creating an inventory database to keep item names descriptions list prices and available quantities. Which Azure SQL Server data type is best suited to store an item price so that decimal precision is kept exactly?

• ❏ A. MONEY

• ❏ B. INT

• ❏ C. DECIMAL or NUMERIC

• ❏ D. VARCHAR

Which type of nonrelational datastore supports a schemaless design and stores entities as JSON objects while keeping all of an entity’s data within a single document?

• ❏ A. Wide column store

• ❏ B. Document database

• ❏ C. Graph database

• ❏ D. Time series database

Identify the missing term in the sentence that follows within the setting of a cloud data platform. A(n) [?] is a component of code that connects to a particular data store and allows an application to read and write that store. A [?] is typically distributed as part of a client library that you can load into the Acme Analytics runtime environment. What is the missing term?

• ❏ A. Hot partition

• ❏ B. Client driver

• ❏ C. Execution thread

• ❏ D. Stored routine

Identify the missing terms in the following Microsoft Azure statement. Synapse orchestrations use the same data integration engine that is used by another service. This enables Synapse Studio to build pipelines that can connect to more than 110 sources including flat files, relational databases, and online services. You can author codeless data flows to carry out complex mappings and transformations as data is ingested into your analytics environment. What are the missing terms?

• ❏ A. Synapse Spark and Azure Databricks

• ❏ B. Cloud Dataflow and Dataproc

• ❏ C. Synapse Pipelines and Azure Data Factory

• ❏ D. Synapse Spark pools and Azure SQL pools

Fill in the missing term for Contoso Cloud with this sentence. [?] is a type of data that maps neatly into tables and requires a rigid schema. Each row in those tables must conform to the specified schema. Multiple tables are commonly connected through relationships. What term completes the sentence?

• ❏ A. Unstructured data

• ❏ B. Document oriented data

• ❏ C. Semi structured data

• ❏ D. Structured data

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

In a globally replicated document database service you can choose how to handle temporary inconsistencies. Which consistency level name describes a mode that causes a delay between when updates are written and when they become readable and allows you to set that staleness as a time interval or as a count of prior versions?

• ❏ A. Consistent Prefix

• ❏ B. Eventual

• ❏ C. Bounded Staleness

• ❏ D. Strong

• ❏ E. Session

A regional retail analytics team at Meridian Insights needs a graphical project based tool that supports offline database project development and visual schema modeling. Which tool should they use?

• ❏ A. Azure Databricks

• ❏ B. Microsoft SQL Server Management Studio (SSMS)

• ❏ C. Microsoft SQL Server Data Tools (SSDT)

• ❏ D. Azure Data Studio

Which web based environment in Microsoft Azure lets a user interactively build pools and pipelines and also lets them develop test and debug Spark notebooks and Transact SQL jobs while monitoring running operations and managing serverless or provisioned compute resources?

• ❏ A. Azure Databricks

• ❏ B. Azure Synapse Link

• ❏ C. Azure Synapse Studio

• ❏ D. Azure Data Factory

• ❏ E. Azure Synapse Analytics

A data engineering team at Contoso Cloud uses a document data store to manage objects data values and named string fields within an entity and the store also supports [?] which is a compact lightweight data interchange format that comes from a subset of JavaScript object literal notation?

• ❏ A. Key Value

• ❏ B. Time series

• ❏ C. JSON

• ❏ D. Graph

Within Microsoft Azure what does the phrase “data egress” describe?

• ❏ A. The process of moving data into Azure from external systems

• ❏ B. Cloud Storage fees

• ❏ C. The transmission of data from Azure environments to locations outside Azure

• ❏ D. The volume of data processed by Azure compute or analytics services

Which Microsoft Azure service best fills the blank in the following statement when considering Azure capabilities? [?] is applicable for scenarios like supply chain analytics and forecasting, operational reporting, batch data integration and orchestration, real-time personalization, and IoT predictive maintenance?

• ❏ A. Azure Data Factory

• ❏ B. Azure Synapse Link

• ❏ C. Azure Databricks

• ❏ D. Azure Synapse Spark

A web portal operated by Riverview Analytics stores its data in an Azure SQL database and requires encrypted connections from the application. Which connection string parameter should be set to require encryption?

• ❏ A. TrustServerCertificate=True

• ❏ B. Encrypt=True

• ❏ C. Encrypt=False

• ❏ D. Integrated Security=True

A supply chain analytics team at Aurora Logistics requires a managed service that handles the full machine learning lifecycle such as experiment tracking model training and production deployment. What role does Azure Machine Learning fulfill in that type of workflow?

• ❏ A. To automate the ingestion of data from multiple sources into storage systems

• ❏ B. BigQuery

• ❏ C. To offer a managed cloud platform for developing training and deploying machine learning models at scale

• ❏ D. To build interactive dashboards for exploring data and presenting insights

Within an Azure deployment at Meridian Analytics what term completes the sentence “_ access allows users to view data but prevents them from changing existing records or creating new ones”?

• ❏ A. Owner role

• ❏ B. Read and write access

• ❏ C. Read only access

• ❏ D. Contributor role

A regional fintech company named Harborbridge plans to adopt a Zero Trust security approach across its cloud and on premises environments. Which principle best represents the Zero Trust philosophy?

• ❏ A. Require Multi Factor Authentication using Azure AD Conditional Access

• ❏ B. Use Google Cloud Identity Aware Proxy to gate access to web applications

• ❏ C. Treat every access attempt as untrusted and authenticate and authorize each request

• ❏ D. Mandate that all user accounts are provisioned in Azure Active Directory for access

A team at Northbridge Solutions is designing a relational schema for a client management platform on Cloud SQL for PostgreSQL and they need a modeling approach that can represent intricate many to many associations among accounts contacts prospects and deals. Which data modeling technique is most suitable for that use case?

• ❏ A. Cloud Spanner schema design

• ❏ B. Dimensional modeling

• ❏ C. Entity Relationship modeling

• ❏ D. Anchor modeling

On the Contoso Cloud platform what distinguishes a “data lake” from a “delta lake”?

• ❏ A. There is no practical difference and the terms refer to the same architecture

• ❏ B. A data lake is object storage such as Cloud Storage and a delta lake is a query service such as BigQuery

• ❏ C. A data lake is a storage repository for raw data and a delta lake is a storage layer that provides ACID transactions and schema enforcement

• ❏ D. A data lake stores raw datasets while a delta lake stores only curated or processed data

Which service enables authors to build and distribute interactive reports and dashboards from their data?

• ❏ A. Looker Studio

• ❏ B. Databricks

• ❏ C. Power BI

• ❏ D. Cloud Dataflow

Which kind of data does Contoso Table service primarily store?

• ❏ A. Relational data

• ❏ B. Unstructured data

• ❏ C. Key-value pairs

• ❏ D. Wide-column data

Choose the missing terms that complete the sentence about Microsoft Azure data workflows. The latency for [A] is normally several hours whereas [B] operates continuously and finishes in the order of seconds or milliseconds. Which terms belong in the blanks?

• ❏ A. [A] Microbatching, [B] Bulk ingestion

• ❏ B. [A] Stream processing, [B] Batch processing

• ❏ C. [A] Scheduled batch processing, [B] Real time streaming

• ❏ D. [A] On demand analytics, [B] Event ingestion

Identify the missing term that completes this sentence in a Google Cloud context. [?] handle situations where two users attempt to work on the same database record at the same time. [?] stop another process from reading the data until the lock is released. This can lead to degraded performance while applications wait for the lock to be cleared?

• ❏ A. Checksums

• ❏ B. Cloud Spanner

• ❏ C. Application servers

• ❏ D. Database locks

Which term best completes this sentence in the context of Contoso Cloud data workflows regarding a process used for building advanced models that draw on many records in storage and that typically execute as scheduled batch jobs?

• ❏ A. Extract, Transform, and Load (ETL)

• ❏ B. Cloud Dataflow

• ❏ C. Atomicity Consistency Isolation and Durability (ACID)

• ❏ D. Extract Load and Transform (ELT)

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

Which class of database is best suited for these scenarios IoT telemetry, online retail and marketing, multiplayer gaming, and web and mobile applications?

• ❏ A. Cloud Bigtable

• ❏ B. Relational database systems

• ❏ C. Nonrelational NoSQL databases

• ❏ D. Cloud Spanner

At a cloud data platform used by a retail analytics firm called Aurora Analytics the engineers use a lakehouse pattern. How is raw unprocessed data usually stored before the data pipelines clean or transform it?

• ❏ A. BigQuery tables

• ❏ B. Cloud Storage objects

• ❏ C. Cloud Pub/Sub messages

Which cloud storage service allows access control lists to be applied to individual files and directories?

• ❏ A. Azure Blob Storage

• ❏ B. Google Cloud Storage

• ❏ C. Azure Data Lake Storage Gen2

• ❏ D. Azure Cosmos DB

Your team manages an Azure SQL Database elastic pool for an online booking service at example.com. One database in the pool is repeatedly consuming far more compute and storage resources than the others and it is degrading the performance of the rest of the databases in the pool. What is the best action to resolve this problem?

• ❏ A. Increase the performance tier of the elastic pool

• ❏ B. Keep the elastic pool unchanged because pooling automatically balances workloads

• ❏ C. Move the high usage database to its own single database outside the pool

• ❏ D. Remove the problematic database from service

A retail analytics team uses the Contoso Cosmos DB platform which API allows developers to issue queries using the Cassandra Query Language CQL?

• ❏ A. Gremlin API

• ❏ B. Cloud Bigtable

• ❏ C. Cassandra API

• ❏ D. SQL API

What role does a “delta lake” storage layer play in the Acme Analytics platform?

• ❏ A. Vertex AI Model Registry

• ❏ B. To provide ACID transactional guarantees and dependable version history for files in a data lake

• ❏ C. To act as a distinct tier for long term archival storage

• ❏ D. To automatically tune queries and build indexes for very large datasets

Which platform should Contoso use to build scalable interactive dashboards for exploring and visualizing business datasets?

• ❏ A. Azure Synapse Analytics

• ❏ B. Azure Data Factory

• ❏ C. Power BI

• ❏ D. Azure Machine Learning

Complete the blanks in the following Azure statement. Hosting [A] on virtual machines allows you to run full instances of [A] in the cloud while you avoid managing any local server hardware. This demonstrates the [B] deployment model?

• ❏ A. Microsoft 365 and Platform as a Service

• ❏ B. SQL Server and Platform as a Service

• ❏ C. Microsoft 365 and Desktop as a Service

• ❏ D. SQL Server and Infrastructure as a Service

Assess whether each of the following statements about a cloud managed SQL database service used by Northbridge Systems is true or false. The statements are that the service provides automated backups with the ability to restore to a point in time within the retention period that it offers built in high availability with automatic failover and redundancy and that it can integrate with an Advanced Threat Protection capability?

• ❏ A. Yes, No, Yes

• ❏ B. Yes, No, No

• ❏ C. Yes, Yes, Yes

Which statement best describes a typical property of nonrelational data stores when compared with traditional relational systems?

• ❏ A. Nonrelational systems require strict data normalization to avoid duplicated information

• ❏ B. Nonrelational databases store data in fixed flat tables with uniform columns for every record

• ❏ C. Nonrelational databases are schema flexible and allow each record to include only the fields it needs

• ❏ D. Nonrelational databases automatically provide full ACID transactions across multiple independent documents by default

Which Azure Data Factory element causes a pipeline to run?

• ❏ A. Pipeline parameter

• ❏ B. Control flow element

• ❏ C. Pipeline trigger

• ❏ D. Pipeline activity

Relational databases are among the most familiar models for storing data in a corporate cloud environment. The table and column layout makes them straightforward to adopt at first and the strict schema can create challenges as applications grow. Which process is used to reorganize data to eliminate redundancy and split records across many narrow tables?

• ❏ A. Denormalization

• ❏ B. BigQuery

• ❏ C. Normalization

• ❏ D. Centralization

A retail analytics firm named Harbor Insights generates a continuous stream of application log events and it needs to ingest and process them in real time to power dashboards and alerts. Which Azure service is most appropriate for ingesting high volume streaming logs and enabling downstream real time processing?

• ❏ A. Azure Databricks

• ❏ B. Azure Event Hubs

• ❏ C. Azure IoT Hub

• ❏ D. Azure Data Factory

Can you deploy SQL Server on a Linux host when using Contoso Cloud services?

• ❏ A. No, SQL Server runs only on Windows Server

• ❏ B. Yes, Contoso Cloud provides SQL Server 2019 and 2022 images for Linux

• ❏ C. Use Cloud SQL for SQL Server as a managed hosted option

What capability must be available for an extract load transform implementation to operate correctly?

• ❏ A. Dataflow

• ❏ B. Source data that is completely transformed before loading into the destination

• ❏ C. A destination data platform that can execute transformations after data is loaded

• ❏ D. An orchestration workflow that moves data and invokes transformations in separate tools

A regional payments startup uses Azure for its staging environments and needs to hide customer identifiers while keeping test datasets realistic and usable. What is the primary purpose of applying data masking in an Azure data platform?

• ❏ A. Compress datasets to lower storage requirements

• ❏ B. Google Cloud Data Loss Prevention

• ❏ C. Encrypt data at rest and in transit for stronger protection

• ❏ D. Mask sensitive fields by replacing them with realistic non-sensitive values to keep datasets usable for testing

The correct answer is Use a per item random hash as the partition key.

Use a per item random hash as the partition key produces a high cardinality and uniformly distributed key space which helps Cosmos DB spread write throughput across many physical partitions. A synthetic random hash avoids concentrating writes on a small set of partition key values and prevents hot partitions during sustained heavy ingest.

Partition by the review submission timestamp is incorrect because timestamps tend to be sequential or clustered and they can concentrate writes into a small number of partitions during bursts. This pattern leads to hot partitions and uneven throughput consumption.

Cloud Bigtable is incorrect because it is a different database service and not a partition key strategy for Cosmos DB. It does not answer the question about how to choose a partition key to balance write traffic in Cosmos DB.

Partition by product SKU is incorrect because product SKUs are often skewed by popularity and they can concentrate writes on a few hot SKUs. That skew creates uneven partition load and does not provide the uniform distribution needed for heavy ingest.

When you face heavy ingest think about cardinality and uniform distribution. Consider a synthetic random hash partition key when natural keys are likely to be skewed.

The correct answer is Data Engineer.

A Data Engineer is responsible for designing testing and maintaining databases and data structures and for aligning the data architecture with business objectives and for acquiring data and developing processes to create and retrieve information from datasets while using programming languages and tools to analyze the data.

A Data Engineer builds and operates scalable ingestion and processing pipelines and implements storage and retrieval systems so that analysts and machine learning practitioners can get reliable data. This role emphasizes architecture design data modeling ETL or ELT processes and optimization for large scale analytics.

Machine Learning Engineer is focused on developing training workflows and deploying machine learning models and model serving infrastructure rather than on general database design and data ingestion pipelines.

Database Administrator concentrates on day to day database operations configuration tuning backups and security for database systems and may not design end to end data pipelines or analytic architecture.

Data Analyst analyzes and interprets data to produce insights and reports and uses tools for exploration and visualization but typically does not build or maintain the underlying data architecture and ingestion processes.

When a question lists tasks like designing data architecture building ingestion pipelines and developing processes to create and retrieve datasets look for the Data Engineer role as the best match.

Azure Blob Storage is the correct choice for storing backup copies and long term archives of large unstructured files.

Azure Blob Storage is an object storage service that scales to store very large amounts of unstructured data. It supports access tiers for hot, cool, and archive data so you can optimize cost for long term retention, and it provides lifecycle management, snapshots, immutability features, redundancy options, and server side encryption which are useful for backup and archival scenarios.

Azure Disk Storage is not ideal because it provides block storage for virtual machine disks and is intended for low latency OS and data disks rather than large scale object archives or long term backup stores.

Azure Cool Storage Tier is not a separate storage offering. It is an access tier within Blob Storage for infrequently accessed data, so the full service is still Azure Blob Storage rather than a standalone product called Cool Storage Tier.

Azure Files Storage offers managed SMB and NFS file shares and is aimed at lift and shift file workloads and shared file systems. It is not optimized for massive object archives and does not provide the same object tiering and lifecycle features as Blob Storage.

When a question asks about large unstructured data plus long term retention look for the object storage service. Remember that hot, cool, and archive are tiers within Azure Blob Storage rather than separate storage products.

Server-level firewall is the correct option for every Azure SQL database.

Server-level firewall is the platform provided access control that is active by default for Azure SQL servers and their databases. Administrators must create server or database firewall rules to allow client IP addresses or enable the built in option to allow Azure services to connect. The firewall blocks incoming connections until appropriate rules are added so it acts as the default network level control for the service.

Azure Firewall is a separate managed network firewall service that you must deploy and configure and it is not automatically applied to each Azure SQL database.

Network security group filters traffic at the subnet or network interface level within a virtual network and it does not serve as the platform default access control for the PaaS Azure SQL endpoint.

Cloud Armor is a Google Cloud product for DDoS and web application protection and it is not relevant to Azure services and it is not used as a default control for Azure SQL.

When you see questions about default access controls for Azure SQL look for references to the server-level firewall or built in firewall rules and rule out network services that must be configured separately.

The correct answer is Azure Stream Analytics.

Azure Stream Analytics is a managed, low latency streaming analytics service that is designed to evaluate event and transaction streams in real time and produce immediate results for scenarios such as fraud detection. It provides a SQL like query language for windowed aggregations and pattern detection and it integrates directly with Event Hubs, IoT Hub, and other ingestion services to process transactions as they arrive.

Azure Stream Analytics also supports calling out to external model endpoints or user defined functions so you can apply trained machine learning models for scoring in the streaming pipeline with minimal operational overhead. That makes it a good fit when you need immediate, continuously running fraud detection rather than periodic batch scoring.

Cloud Dataflow is not the right choice because it is a Google Cloud service built around Apache Beam and it is not the native Azure streaming service for this scenario.

Azure Machine Learning is focused on training, managing, and hosting models and it can serve real time endpoints but it is not itself a managed streaming analytics engine for applying models across a continuous event stream.

Azure Databricks can perform streaming analytics with Structured Streaming and it can apply models, but it requires more engineering and cluster management and it is generally more complex than using a purpose built managed streaming engine when you need low latency, production ready fraud detection.

Read the question for keywords such as real time and streaming and then pick the managed streaming analytics service rather than the model training or batch processing service.

The correct answer is Pioneer Table Storage.

Pioneer Table Storage matches the requirements because it offers a table style, key based storage model that supports semi structured entries and avoids complex relational mappings. It is designed so that inserting a row has constant performance regardless of table size and a storage container can hold up to 250 TB of data. Row writes are fast and reads are efficient when queries use the partition and row keys which directly matches the scenario.

Google Cloud Spanner is incorrect because Spanner is a globally consistent, relational database that expects schemas and relational design. It is optimized for transactional consistency and SQL workloads rather than lightweight table style key value or wide column access patterns.

Cloud SQL for PostgreSQL is incorrect because Cloud SQL is a managed relational database. It requires schema management and relational mappings and insert performance can be affected by indexes and table design which makes it a poor fit for the described table style, schema flexible workload.

Google Cloud Bigtable is incorrect in this question even though it shares many characteristics with table style NoSQL stores. Bigtable is a wide column, high throughput store that scales to petabytes and uses row keys for efficient reads. The question specifically describes a product with a 250 TB container limit and the simple table API semantics that match Pioneer Table Storage more closely than Bigtable.

Cloud SQL for MySQL is incorrect because like other Cloud SQL offerings it is a relational database and it requires relational schemas and mapping. That design does not align with the simple, semi structured, key based table storage described in the question.

Look for keywords such as partition and row keys and no complex relational mappings to identify NoSQL table stores. Those phrases usually point to a key value or wide column table service rather than a relational database.

The correct answer is Advanced Data Security.

Advanced Data Security refers to a suite of controls that go beyond simple authentication and authorization to provide data discovery and classification, vulnerability assessment, and threat detection as well as audit logging and contextual alerts. These capabilities are designed to add layers of protection and detection around data services so that risks can be found and mitigated in addition to enforcing who can access resources.

Cloud Key Management Service is incorrect because it primarily handles encryption key creation, storage, and lifecycle management and it does not by itself provide the broader discovery, assessment, and threat detection features that are implied by the phrase Advanced Data Security.

VPC Service Controls is incorrect because it focuses on creating a network perimeter to reduce data exfiltration risk and it is not a catch all suite for vulnerability assessment and data classification which are the hallmarks of Advanced Data Security.

Cloud SQL Managed Instance is incorrect because it is a managed database offering rather than a security feature set that provides additional data protection services beyond authentication and authorization.

When a question asks about protections beyond authentication and authorization look for options that describe suites of detection, classification, and assessment features and not just single capabilities like key management or network perimeters. Keep an eye out for phrases like vulnerability assessment and data discovery.

The correct option is SQL Server Management Studio.

SQL Server Management Studio provides a comprehensive graphical interface that lets you run queries and manage database objects using Object Explorer and a full query editor. It also includes tools to perform common database administration tasks such as backup and restore, security management, and performance monitoring and it can produce T SQL scripts to automate maintenance and support operations.

SQL Server Management Studio connects to on premise SQL Server and Azure SQL resources and it is the traditional full featured tool that database administrators use for administrative workflows and scripting.

Azure Synapse Studio is focused on analytics and data integration for big data and data warehousing and it is not intended as a general purpose DBA graphical tool for managing transactional SQL Server databases.

Azure Data Studio is a cross platform, lightweight editor that is great for queries, notebooks, and development tasks but it does not provide the same deep administrative feature set and automated maintenance tooling that SQL Server Management Studio offers.

Azure Portal Query Editor is a browser based, quick query tool for simple edits and troubleshooting and it lacks the full object explorer, administrative task GUIs, and scripting features required for comprehensive database administration.

When a question asks for a full graphical administration tool pick the option that explicitly mentions management or server administration and not lightweight editors. SQL Server Management Studio is the full featured DBA tool for those tasks.

The correct answers are Azure Synapse Analytics and Azure Data Factory.

Azure Synapse Analytics combines enterprise data warehousing with big data processing so it can host relational, analytical workloads and run distributed processing over data in a data lake. This makes it suitable for the traditional warehousing part of the solution and for large scale analytics using integrated SQL and Spark engines.

Azure Data Factory provides managed data ingestion and orchestration for batch and streaming sources and it can move, transform, and schedule data into a data lake or a warehouse. This makes it the right choice to build the pipeline that ingests data and coordinates processing in Synapse or other compute services.

Azure SQL Database is a transactional relational database that is optimized for OLTP workloads and single database scenarios. It is not designed as a scalable data warehouse or a distributed big data processing engine so it does not fit the described pipeline.

Azure Databricks is a powerful analytics and Spark platform that is often used for big data processing and machine learning. It is not marked as a correct option here because the question focuses on the integrated warehouse and orchestration pair and Databricks is an alternative compute option rather than the primary ingestion and orchestration services named in the correct answers.

Azure Stream Analytics is a real time stream processing service that handles event queries and simple stream transformations. It is useful for streaming scenarios but it does not provide the full warehousing and batch orchestration capabilities that Synapse and Data Factory provide together.

Azure Cosmos DB is a globally distributed NoSQL database that is optimized for low latency transactional access. It is not a data warehouse or a distributed analytics engine so it is not the right fit for the ingestion and processing pipeline described.

When you see pipeline questions look for one service that handles orchestration and ingestion and another that provides integrated warehousing or scalable analytics. Pay attention to whether the service is optimized for OLTP or for analytics and choose the analytics pair when ingestion and processing are both required.

NoSQL is correct because the four types listed in the sentence are the standard classification of NoSQL databases.

NoSQL systems are built for flexible schemas and horizontal scalability. Key value stores hold simple key and value pairs and document databases store semi structured documents often using JSON. Column family databases organize data into wide rows and column families and graph databases model relationships explicitly. These four types together describe the NoSQL category which makes NoSQL the best fit for the sentence.

SQL is incorrect because it refers to the structured query language and to relational databases that use fixed schemas and tables rather than the NoSQL types named in the question.

Cloud Bigtable is incorrect because it names a specific Google Cloud service that implements a wide column family style NoSQL store and it is not the general category that includes key value document column family and graph databases.

JSON is incorrect because it is a data interchange format commonly used by document databases and APIs and it is not a category of databases.

When a question lists types such as key value document column family and graph remember that those are types of NoSQL databases rather than query languages, specific services, or file formats.

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

Hot tier, Cool tier, Archive tier is correct.

The Hot tier is intended for frequently accessed objects and provides the lowest retrieval latency with higher storage cost. The Cool tier is for less frequently accessed data and balances lower storage cost with somewhat higher access costs. The Archive tier is for long term retention and offers the lowest storage cost with the highest retrieval latency and potential retrieval charges.

These three tiers map to decreasing access frequency and decreasing storage cost and they form the common tiering model used by many cloud object storage services including Azure Blob Storage which uses Hot, Cool, and Archive names.

Nearline tier, Coldline tier, Archive tier is incorrect because those names reflect a different provider naming convention and do not match the Hot, Cool, Archive trio required by the question.

Static tier, Fluid tier, Hybrid tier is incorrect because those terms are not standard object storage access tier names and they do not represent the familiar hot cool archive model.

Hot tier, Warm tier, Cold tier is incorrect because while it uses a hot category it replaces Cool and Archive with Warm and Cold which changes the intended cost and retrieval characteristics and does not match the expected Hot, Cool, Archive grouping.

Read each option exactly and match the names used for access tiers rather than just the concept. Remember that Hot, Cool, and Archive indicate decreasing access frequency and decreasing storage cost.

The correct answer is Azure SQL Edge.

Azure SQL Edge is specifically designed for Internet of Things scenarios and for processing streaming time series data on or near devices. It provides a small footprint that runs on ARM and x86 hardware and it includes time series and streaming ingestion features, local analytics, and integration with Azure IoT services so you can process data with low latency and operate when connectivity is intermittent.

Azure SQL Database is a fully managed cloud database service that targets general purpose and hyperscale cloud workloads and it is not aimed at device level edge deployments or offline streaming time series processing.

SQL Server on Azure Virtual Machines gives you full control over the operating system and SQL Server instance and it is an infrastructure solution for lift and shift migrations. It is not optimized for constrained IoT edge devices or for built in edge streaming time series features.

Azure SQL Managed Instance provides near full compatibility with SQL Server in a managed PaaS form and it is intended for migrating instance scoped workloads to Azure. It is a cloud managed instance and it does not target edge device streaming time series processing.

Pay attention to keywords like edge, IoT, and streaming time series in the question. Those words usually indicate Azure SQL Edge as the intended choice.

The correct answer is Contoso Stream Analytics.

Contoso Stream Analytics is purpose built for real time processing of high velocity telemetry and for performing immediate analysis with low latency. It runs continuous queries over incoming data streams and supports windowing and pattern matching which makes it suitable for analyzing tracker data as it arrives.

Contoso Stream Analytics also integrates with scalable ingestion services so you can ingest events at high throughput and run analytics jobs on that stream to produce alerts, aggregates, or outputs to dashboards and storage in near real time.

Contoso Data Factory is an orchestration and ETL service that is designed for batch or scheduled data movement and transformation. It is not intended for low latency continuous stream processing or immediate analysis of high velocity telemetry.

Contoso Machine Learning focuses on building, training, and deploying models and it does not provide the continuous stream processing engine needed to ingest and analyze high velocity telemetry on the fly. You could use models with a streaming pipeline, but this service alone is not the real time analytics engine.

Contoso Event Hubs is an event ingestion platform that can receive high velocity streams and serve as the front door for telemetry. It does not perform the analytical processing itself, so it needs to be paired with a stream processing service to achieve immediate analysis.

When a question mentions real time or immediate analysis choose a stream processing service that runs continuous queries rather than a batch ETL or a model training service.

The correct answer is Dataset represents the data structure stored in a source or sink. Linked service holds the connection details and credentials needed to access external systems. Pipeline is a logical collection of activities that execute together and can be scheduled.

This option is correct because a dataset describes the data shape and location that activities consume or produce. A linked service stores the connection information and credentials that enable the integration service to reach external stores. A pipeline groups activities for orchestration and can be triggered or scheduled to run as a unit.

Dataset is a model of how data is organized inside storage. Mapping data flow is the connection information used to reach external systems. Pipeline is a scheduled grouping of tasks is incorrect because it swaps the role of mapping data flow with connection details. Mapping data flows define transformations and do not hold credentials. Also dataset and pipeline definitions in that option are either imprecise or misassigned.

Linked service models the shape of data in stores. Mapping data flow performs visual data transformations at scale. Pipeline is a logical grouping of activities is incorrect because a linked service does not model data shape. The data shape is modeled by datasets. Mapping data flows do perform visual transformations but that does not make the linked service description correct.

Dataset represents the data layout. Linked service stores connection details. Mapping data flow refers to Google Cloud Dataflow rather than transformation definitions. Pipeline is a grouping of activities is incorrect because mapping data flow is an Azure Data Factory concept for defining transformations and not a reference to Google Cloud Dataflow. The wording also mixes platform terms which makes the mapping inaccurate for this context.

When you see the trio dataset, linked service, and pipeline think of data shape, connection and credentials, and orchestration and scheduling respectively.

The correct option is JSON.

A JSON document represents objects with curly brackets and arrays with square brackets and fields are named and separated from their values by the word colon. Strings are wrapped in quotes and fields are separated by commas which matches the description provided in the question. JSON is a lightweight, text based data interchange format and these syntactic elements are its defining characteristics.

HTML is a markup language for structuring web pages and it uses angle bracket tags and attributes rather than curly brackets and comma separated fields, so it does not match the described syntax.

YAML is a human friendly data serialization format and it often uses indentation and dash prefixes for lists instead of requiring curly brackets and commas, so it is not the best match for the quoted description.

XML uses nested angle bracket elements and attributes and it does not use curly brackets, square brackets, quoted strings and comma separated fields in the way described, so it is not the correct answer.

When a question mentions curly brackets, square brackets, quotes and commas look for JSON as the likely format.

Azure Stream Analytics is correct because it is designed for low latency processing of continuous event streams and it provides built in machine learning scoring capabilities.

Azure Stream Analytics is a managed real time analytics service that ingests events from sources such as Event Hubs and IoT Hub and executes SQL like queries with windowing to produce low latency results. It supports real time scoring by calling Azure Machine Learning endpoints and by using built in functions that apply models to streaming data so you can do machine learning inference as part of the streaming pipeline.

Azure Synapse Analytics is incorrect. Synapse focuses on large scale data warehousing and integrated analytics across batch and interactive workloads rather than low latency continuous event stream processing and built in streaming ML scoring.

Azure Databricks is incorrect. Databricks offers a unified analytics platform based on Apache Spark and it can process streams and run machine learning pipelines, but it is aimed at notebooks and heavy data engineering workloads and it does not provide the same managed low latency streaming service with built in scoring out of the box.

Azure Machine Learning is incorrect. Azure Machine Learning is focused on training, managing and deploying models and it provides endpoints for scoring, but it is not itself a managed stream processing engine for low latency continuous event streams.

When a question mentions real time or low latency and continuous event streams think of Azure Stream Analytics. Match the service to its primary use case to eliminate distractors.

Primary key and Foreign key are required to model a one-to-many relationship between two tables.

The Primary key uniquely identifies each row in the parent table and provides the target that child rows reference. Without a primary key the parent row cannot be reliably referenced and the relationship would not be properly enforced.

The Foreign key is stored in the child table and holds values that match the parent table primary key so many child rows can point to a single parent row. The foreign key enforces referential integrity and lets the database ensure that relationships remain consistent.

The Index can speed lookups on key columns but it does not define or enforce relationships by itself. An index is an optimization structure and it can exist without representing a one to many relationship.

The Unique constraint prevents duplicate values in a column and can be used to enforce alternate keys but it does not by itself create a reference from a child table to a parent table. A unique constraint on the parent may be referenced by a foreign key but the foreign key is still required to model the one to many relationship.

When you see questions about relationships look for one option that provides a unique identifier and another that provides a reference from the child table. Those two together form the one to many link.

DECIMAL or NUMERIC is correct because these types store fixed precision and scale and so they preserve decimal precision exactly for prices.

DECIMAL or NUMERIC lets you specify the total number of digits and the number of digits after the decimal point and it performs arithmetic as exact decimal math which is important for currency and pricing. These types are part of the SQL standard and are portable across systems which helps avoid rounding errors that can occur with binary floating point types.

MONEY is a SQL Server currency type but it has a fixed scale and can introduce subtle rounding and formatting issues and it is less portable across different database systems, so it is not the preferred choice when you need explicit control over precision and scale.

INT stores whole numbers only so it cannot represent cents or fractional currency without manual scaling and that approach is error prone and can lead to loss of precision in calculations.

VARCHAR stores text and so it cannot enforce numeric precision or perform numeric operations without converting the value back to a numeric type, which risks invalid values and inconsistent sorting and is not appropriate for prices.

When storing monetary values choose a numeric type that preserves both precision and scale. Use DECIMAL or NUMERIC for exact currency storage and avoid strings or floating point types for prices.

The correct answer is Document database.

Document database systems are schemaless and store entities as JSON or JSON like documents so all of an entity’s fields and any nested data remain together in a single document. This model allows you to read or write an entire entity with one operation which matches the description in the question.

Wide column store systems organize data into rows with dynamic columns and they are optimized for sparse data and wide tables rather than storing each entity as a single JSON document. They use column families and a different storage model.

Graph database systems model data as nodes and edges and they are optimized for relationship queries and traversals. They do not primarily store whole entities as JSON documents in a single document.

Time series database systems are specialized for timestamped metrics and events and they focus on efficient storage and querying of sequences over time rather than schemaless JSON documents for full entities.

Watch for keywords like schemaless and JSON or phrases about keeping all an entity’s data together in one record. Those clues usually point to a document database.

The correct answer is Client driver.

A Client driver is a component of code that applications load into their runtime to speak the protocol of a particular data store and to provide read and write operations. It is commonly distributed as part of a client library that you include with your application and it handles connection management, authentication, serialization, and the low level protocol details so you do not have to implement them yourself.

Hot partition is incorrect because that term describes a shard or partition receiving disproportionate traffic and it is not a piece of client code that connects to a data store.

Execution thread is incorrect because that term refers to a unit of execution in a program or runtime and it does not represent a library or driver that implements datastore connectivity.

Stored routine is incorrect because that term means a procedure or function stored and executed inside the database itself and it is not distributed as a client library for use in the application runtime.

Focus on the words component of code and distributed as part of a client library when you read similar questions. Those phrases usually point to a client driver or client library rather than runtime threads or data layout concepts.

The correct answer is Synapse Pipelines and Azure Data Factory.

Synapse Pipelines and Azure Data Factory is correct because Synapse orchestrations are built on the same data integration engine as Azure Data Factory. This shared engine is what gives Synapse Studio access to more than 110 connectors and the ability to author codeless data flows for complex mappings and transformations as data is ingested into your analytics environment.

Synapse Spark and Azure Databricks is incorrect because those names refer to Spark compute environments and managed Databricks clusters. They are focused on processing and analytics rather than acting as the orchestration and integration engine that provides the wide connector set and codeless mapping features.

Cloud Dataflow and Dataproc is incorrect because those are Google Cloud Platform services. They are not part of Azure and therefore do not describe the integration engine used by Synapse.

Synapse Spark pools and Azure SQL pools is incorrect because those options describe compute pools and database pools. They are not the data integration service that orchestrates pipelines, manages connectors, and provides codeless data flows.

When a question mentions a shared integration engine and many connectors, think of Azure Data Factory and Synapse Pipelines rather than compute or storage services.

The correct option is Structured data.

The description given matches Structured data because this type of data maps neatly into tables and requires a rigid schema. Each row in those tables must conform to the specified schema and multiple tables are commonly connected through relationships as in relational databases.

Unstructured data is incorrect because it refers to content that does not follow a predefined model. Examples include free text, images, audio, and video and such data does not fit neatly into fixed tables.

Document oriented data is incorrect because it stores records as documents such as JSON or BSON and it supports flexible or varying schemas rather than a strict, table based schema required by the question.

Semi structured data is incorrect because it uses tags or keys to convey structure for example XML or JSON and it allows variable attributes and nesting rather than requiring every row to match a strict table schema.

When a question mentions rigid schema and tables and rows think structured data.

Bounded Staleness is correct because it deliberately allows a delay between when updates are written and when they become readable and it lets you configure that staleness as either a time interval or as a count of prior versions.

Bounded Staleness provides a predictable and configurable window of staleness so replicas will not lag beyond the configured time or number of updates and you can tune this to balance latency and consistency for globally replicated document databases.

Consistent Prefix is incorrect because it only guarantees that reads will see writes in order and it does not provide a configurable bound expressed as a time interval or a count of versions.

Eventual is incorrect because it offers no bounded guarantee and it allows replicas to converge at an unspecified future time so you cannot set a maximum staleness in time or versions.

Strong is incorrect because it gives linearizable reads that always reflect the most recent write and it does not introduce or allow a configurable staleness window.

Session is incorrect because it provides consistency guarantees that are scoped to a single client session such as read your writes and monotonic reads and it does not let you define a global staleness as time or number of versions.

When you map consistency names to behaviors remember that Bounded Staleness is the only level that explicitly lets you configure a maximum staleness either in seconds or in number of versions.

The correct answer is Microsoft SQL Server Data Tools (SSDT).

Microsoft SQL Server Data Tools (SSDT) provides Visual Studio integrated database projects that let a team develop database schemas offline and use visual designers and schema compare tools to model and deploy changes. It is designed for project based development and supports offline editing and source control integration which matches the requirement for graphical project based tooling and visual schema modeling.

Azure Databricks is a cloud analytics and big data platform for running Spark workloads and interactive notebooks and it is not focused on offline project based database development or visual schema designers.

Microsoft SQL Server Management Studio (SSMS) is primarily an administrative and query tool and it lacks the project based database project model and integrated visual schema project tooling that SSDT provides.

Azure Data Studio is a modern cross platform editor that is good for queries and lightweight tasks and it can be extended with extensions but it does not provide the same Visual Studio database projects and offline visual schema modeling that SSDT delivers.

When a question asks for a tool that supports project based and offline database development think of Visual Studio database projects and choose SSDT rather than administration or analytics tools.

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

The correct answer is Azure Synapse Studio.

Azure Synapse Studio is the web based integrated environment in Azure that lets you author and manage data pipelines and build and manage Spark pools and SQL pools from one place. It provides interactive notebooks for developing and debugging Spark code and it supports Transact SQL scripts and jobs while offering monitoring tools to view running operations and to manage serverless or provisioned compute resources.

Azure Databricks is a managed Apache Spark service with its own workspace and notebook experience and it is a separate product from the Synapse web studio.

Azure Synapse Link is intended to connect operational stores to Synapse for near real time analytics and change feed integration and it is not the interactive web studio used to build pools pipelines and notebooks.

Azure Data Factory focuses on cloud native data integration and pipeline orchestration and it does not provide the same integrated notebook based Spark development and T SQL authoring experience in a single web studio.

Azure Synapse Analytics is the broader analytics platform that contains the engines and services and it is the overall offering behind the studio. The question specifically asks for the web based environment and that environment is Azure Synapse Studio rather than the larger platform.

Look for answers that name the specific web based IDE when a question mentions notebooks pipelines monitoring and compute management. If an option is a broader service or a separate product then it is less likely to be the correct choice, and pay attention to the phrase web based environment.

JSON is correct. The document data store described stores objects values and named string fields and it supports a compact lightweight data interchange format that comes from a subset of JavaScript object literal notation and that format is JSON.

Many document databases represent records as JSON documents or as binary variants such as BSON which build on the same object notation. This structure allows nested objects arrays and named fields which matches the description in the question.

Key Value is incorrect because key value stores focus on simple mappings from a single key to a single value and they do not inherently provide the nested document structure and named fields that the question describes.

Time series is incorrect because time series databases are optimized for timestamped sequences of measurements and trending data and they are not defined by a JavaScript object literal style interchange format.

Graph is incorrect because graph databases model nodes edges and relationships and they are focused on connections rather than storing entities as JSON documents.

When a question mentions documents objects and named fields think of JSON and document databases and eliminate options that describe entirely different data models.

The transmission of data from Azure environments to locations outside Azure is the correct option.

This phrase refers to outbound network traffic that leaves Azure and goes to the internet other clouds or on premises locations and it is often subject to outbound bandwidth charges. It is the opposite of data ingress which describes data moving into Azure from outside.

The process of moving data into Azure from external systems is incorrect because that describes ingress not egress. Inbound transfers are into Azure and are not what egress refers to.

Cloud Storage fees is incorrect because egress specifically means data transfer out of Azure and not the general charges for storing data. Storage fees cover storing objects and not the direction of network traffic.

The volume of data processed by Azure compute or analytics services is incorrect because egress is about transmitting data out of the cloud and not about how much data is processed by compute or analytics services. Processing volume relates to compute billing rather than outbound transfer direction.

When a question asks about data transfer focus on the direction of the data. Outbound or leaving Azure means egress and inbound or entering Azure means ingress.

Azure Synapse Link is the correct option because it is designed to enable near real time analytics over operational data stores and it directly addresses scenarios such as supply chain analytics and forecasting, operational reporting, batch data integration and orchestration, real time personalization, and IoT predictive maintenance.

Azure Synapse Link provides a managed path from transactional systems into analytical stores without heavy custom ETL work and it integrates with Synapse Analytics so you can run analytical queries, machine learning and reporting on the operational data with minimal latency.

Azure Data Factory is focused on data movement, transformation and orchestration and it is excellent for batch ETL but it does not itself provide the near real time operational to analytical store connection that Synapse Link offers.

Azure Databricks is a powerful analytics and machine learning platform that supports streaming and batch processing, but the question asks for the managed capability that links operational stores to analytical workloads with minimal ETL which points to Synapse Link rather than Databricks.

Azure Synapse Spark refers to the Spark compute pools within Synapse and it is used for processing and analytics but it is a compute environment rather than the managed connector that replicates or exposes operational data for near real time analytics which is the role of Synapse Link.

When a question mentions near real time analytics or analyzing operational stores with minimal ETL look for features that explicitly connect transactional and analytical systems such as Synapse Link. Focus on the phrasing about operational data rather than general ETL or compute services.

The correct option is Encrypt=True.

Setting Encrypt=True in the connection string tells the client driver to use TLS so that the application connects over an encrypted channel. This is the parameter that requests encryption for the transport layer when connecting to Azure SQL Database and other SQL Server endpoints.

TrustServerCertificate=True is not the right choice because it only controls whether the client validates the server certificate. That option does not by itself request encryption and turning it on can weaken security by allowing an untrusted certificate to be accepted.

Encrypt=False is incorrect because it explicitly disables encryption and would not meet the requirement for encrypted connections from the application.

Integrated Security=True is incorrect because it relates to authentication using Windows credentials and has nothing to do with whether the connection is encrypted.

When a question asks which connection string setting enforces encryption look for the Encrypt keyword. Remember that TrustServerCertificate only affects certificate validation and does not enable encryption on its own.

The correct option is To offer a managed cloud platform for developing training and deploying machine learning models at scale.

Azure Machine Learning is a managed service that supports the end to end machine learning lifecycle including experiment tracking model training model registries and deployment to production endpoints. It provides tools for building pipelines provisioning compute managing models and automating deployment so teams can move from research to production at scale.

To automate the ingestion of data from multiple sources into storage systems is incorrect because that describes data integration or ETL services such as Azure Data Factory or other ingestion tools rather than a managed ML lifecycle platform.

BigQuery is incorrect because it is a Google Cloud data warehouse product and not a managed service for experiment tracking training and deploying machine learning models.

To build interactive dashboards for exploring data and presenting insights is incorrect because dashboarding and BI tools handle visualization and reporting and they do not provide the full set of managed ML lifecycle capabilities that Azure Machine Learning offers.

When a question asks about the full machine learning lifecycle think of an end to end managed platform that covers experiment tracking training model management and deployment and rule out options that describe ingestion or dashboarding.

Read only access is correct because it describes permissions that allow users to view data but prevent them from changing existing records or creating new ones.

Read only access maps to a reader level of permissions that grants the ability to inspect or view resources without write or create capabilities. This prevents modification of existing records and stops users from adding new entries while still allowing them to see the data they need.

Owner role is incorrect because an owner has full control and can change and create resources as well as manage access.

Read and write access is incorrect because it explicitly allows modifications and creation of records so it does not prevent changes.

Contributor role is incorrect because a contributor can create and modify resources which means it is not read only even though it cannot manage role assignments.

When a question focuses on viewing without changing look for words like read or reader and eliminate options that mention write or contribute.

The correct option is Treat every access attempt as untrusted and authenticate and authorize each request.

This option expresses the core Zero Trust philosophy which says that no user or device is implicitly trusted and that each access attempt must be verified and authorized before access is granted.

Zero Trust emphasizes continuous validation, least privilege access, and making access decisions based on contextual signals for each request. Treating every access attempt as untrusted and authenticating and authorizing each request aligns with those guiding principles and covers both user and device posture as well as network controls.

Require Multi Factor Authentication using Azure AD Conditional Access is incorrect because multi factor authentication and conditional access are useful controls that help implement Zero Trust. They are product specific controls and do not by themselves state the overarching Zero Trust principle.

Use Google Cloud Identity Aware Proxy to gate access to web applications is incorrect because Identity Aware Proxy is a specific Google product that can enforce access controls. It can be part of a Zero Trust implementation but it does not express the fundamental philosophy of treating every access attempt as untrusted.

Mandate that all user accounts are provisioned in Azure Active Directory for access is incorrect because centralizing identities can aid management and security but Zero Trust does not require a particular identity provider. The philosophy focuses on verifying and authorizing each request rather than mandating a single provisioning source.

When you see Zero Trust questions look for principle level language and remember the phrase never trust, always verify. Give lower weight to answers that only name a single product or control.

The correct answer is Anchor modeling.

Anchor modeling is a relational modeling technique that is built to represent complex many to many relationships while keeping the schema highly extensible and normalized. It models entities as anchors and relationships as ties which makes it straightforward to express many to many associations among accounts, contacts, prospects, and deals and to add new relationships without heavy schema migrations.

Anchor modeling also supports historization and a high degree of schema agility which is valuable for a client management platform that must track changes over time and evolve rapidly. The pattern maps cleanly to PostgreSQL tables and indexes so it is practical to implement on Cloud SQL for PostgreSQL.

Cloud Spanner schema design is incorrect because it targets Google Cloud Spanner which is a globally distributed NewSQL database with different data modeling practices and performance trade offs. Cloud Spanner design patterns do not directly apply to a Cloud SQL for PostgreSQL implementation.

Dimensional modeling is incorrect because it is optimized for analytical workloads and reporting with star and snowflake schemas. It is not designed to represent transactional many to many relationships with the same flexibility and evolvability that anchor modeling provides.

Entity Relationship modeling is incorrect in this context because it is a general conceptual modeling approach and it does not prescribe the specialized, normalized, and historized patterns that anchor modeling provides for complex many to many relationships and evolving schemas.

When the question asks about modeling complex many to many relations and evolving schemas favor techniques that emphasize extensibility and historization. Think about whether the pattern maps naturally to relational tables and supports incremental changes without costly migrations.

The correct answer is A data lake is a storage repository for raw data and a delta lake is a storage layer that provides ACID transactions and schema enforcement.

A data lake refers to durable object storage that ingests and holds raw structured, semi structured, and unstructured data at scale. A delta lake is a storage layer built on top of that storage and it adds transactional metadata, atomic writes, and schema enforcement so analytic workloads run reliably.

The delta lake approach relies on a transaction log to provide ACID guarantees, support time travel, and enable consistent reads during concurrent writes. Those features are the key distinctions compared with a plain data lake that does not provide transactional semantics.

There is no practical difference and the terms refer to the same architecture is incorrect because the terms describe different roles. One refers to raw storage and the other refers to a transactional layer that enforces consistency.

A data lake is object storage such as Cloud Storage and a delta lake is a query service such as BigQuery is incorrect because a delta lake is not a query service. BigQuery is a managed analytics warehouse and a delta lake is a storage layer that can be used by query engines.

A data lake stores raw datasets while a delta lake stores only curated or processed data is incorrect because a delta lake can contain raw or processed data. The defining trait of a delta lake is the transactional and schema features rather than a restriction to only curated data.

When a question contrasts two similar terms focus on concrete capabilities such as ACID, schema enforcement, or whether something is a storage layer versus a query service.

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

The correct answer is Power BI.

Power BI is a business intelligence and reporting platform that lets authors create interactive reports and dashboards from many data sources and then distribute them to users through the Power BI service and apps. It provides visual authoring tools, data connectors, and sharing features that match the scenario described.

Looker Studio is a Google reporting tool that also builds dashboards and interactive reports, but it is not the answer selected here. The question points to the Microsoft BI product rather than the Google reporting service.

Databricks focuses on big data processing, collaborative notebooks, and machine learning workflows. It is not primarily an authoring and distribution platform for interactive dashboards and reports.

Cloud Dataflow is a managed stream and batch data processing service. It is intended for data pipelines and transformations and does not provide the authoring and sharing features for interactive dashboards described in the question.

When choices mix reporting tools and data processing services choose the product that explicitly mentions reports or dashboards. Look for words like interactive and share in the product description to identify BI tools.

The correct answer is Key-value pairs.

Table services are designed to store simple, schema-less entities that are accessed by a primary key and often a partition key. This maps directly to a collection of Key-value pairs where each entity is a set of properties associated with a unique key. That is why Key-value pairs is the correct choice for the Contoso Table service.

Relational data is incorrect because relational databases rely on fixed schemas, normalized tables, and joins or complex transactions. Table services do not provide the same relational features or query semantics as a relational database.

Unstructured data is incorrect because raw files, images, and blobs are typically stored in object or blob storage rather than in a table service. Table services are intended for structured or semi structured records tied to keys rather than arbitrary unstructured content.

Wide-column data is incorrect because wide-column stores use a different model with rows that can contain many dynamic columns and are optimized for different access patterns. While some table offerings share similarities with wide-column systems, the simplest and most accurate description of Contoso Table service is as a Key-value pairs store.

When you see a Table service question look for keywords such as key or schema-less and favor the key value model for simple lookup and fast retrieval scenarios.

The correct answer is [A] Scheduled batch processing, [B] Real time streaming.

Scheduled batch processing runs on a timetable and accumulates data into large jobs so end to end latency is often measured in hours. Real time streaming ingests and processes events continuously and delivers results in seconds or milliseconds which matches the low latency described for slot B.

[A] Microbatching, [B] Bulk ingestion is incorrect because microbatching refers to small periodic batches that reduce latency to minutes rather than the multi hour delays implied for A and bulk ingestion means large one time loads rather than continuous, low latency processing for B.

[A] Stream processing, [B] Batch processing is incorrect because the roles are reversed. Stream processing is continuous and low latency so it belongs in B, and batch processing is scheduled and higher latency so it belongs in A.

[A] On demand analytics, [B] Event ingestion is incorrect because on demand analytics describes ad hoc queries or interactive workloads rather than regularly scheduled multi hour batch jobs and event ingestion describes the incoming data stream but the pairing does not reflect the conventional scheduled batch versus real time streaming contrast.

Focus on words that imply timing and frequency. Look for scheduled and batch to indicate higher latency and for real time and streaming to indicate continuous low latency.

Database locks is correct because the sentence describes a mechanism that handles concurrent access to the same database record and that can block other processes from reading until the lock is released which leads to degraded performance while applications wait.

Database locks are a database level concurrency control mechanism. Exclusive locks prevent other transactions from reading or writing a locked record and shared locks permit reads while blocking writes. When many clients contend for the same rows locks cause blocking and waiting which degrades performance and can even lead to deadlocks if not managed properly. Some systems use pessimistic locking which explicitly acquires locks while others use optimistic concurrency control to reduce blocking.

Checksums are used to verify data integrity and detect corruption and they do not coordinate concurrent access or prevent other processes from reading or writing records.

Cloud Spanner is a specific managed database service and not the generic mechanism described by the sentence. Spanner provides transactional and timestamp based concurrency controls and its documentation focuses on transactions rather than the general concept of database locks.

Application servers host business logic and can coordinate workflows but they do not implement the database level locking mechanism that blocks reads or writes at the record level. That control is provided by the database itself.

When a question mentions blocking reads and processes waiting for access focus on concurrency control and locks. Watch for keywords like blocking or waiting to map the sentence to Database locks.

Extract Load and Transform (ELT) is correct because it describes loading large volumes of raw records into storage first and then transforming them inside the data warehouse or analytics store, which matches workflows that build advanced models from many stored records and that usually run as scheduled batch jobs.

ELT is well suited to model training and analytics because it allows raw data to be persisted where scalable query engines and SQL based transformations can operate directly on the stored records, and those transformations and feature engineering steps are commonly implemented as scheduled batch jobs.

Extract, Transform, and Load (ETL) is incorrect because it performs transformations before loading into the target store, and that pattern is less common for modern analytics workflows that rely on loading raw data first and transforming inside the warehouse for large scale model building.

Cloud Dataflow is incorrect because it is a specific Google Cloud service for stream and batch data processing and not the general workflow term that describes loading many records into storage and then transforming them for model building.

Atomicity Consistency Isolation and Durability (ACID) is incorrect because it refers to transactional properties of databases and has nothing to do with the described batch model training workflow.

When a question describes loading raw data into storage and then running scheduled batch transformations for analytics or machine learning think ELT rather than ETL.

Nonrelational NoSQL databases is the correct choice for these scenarios.

NoSQL databases scale horizontally and handle very high ingest rates while supporting flexible schemas which makes them a good fit for IoT telemetry. They also support document and key value models that work well for online retail catalogs and marketing data where the schema evolves frequently and for multiplayer gaming and web and mobile applications that need low latency and fast lookups.

Cloud Bigtable is a powerful NoSQL wide column database that excels at large scale time series and low latency workloads. It is a specific product optimized for those patterns and it is therefore a narrower choice than the general class asked for, and it may not be the best fit for transactional retail operations or workloads that require rich secondary indexes.

Relational database systems are built for strong consistency, complex joins, and structured schemas which makes them ideal for transactional workloads. They are not the best general fit for highly scalable, schema flexible, or extremely high ingest scenarios like IoT telemetry and some large scale marketing or gaming workloads.

Cloud Spanner is a globally distributed relational database that provides horizontal scale with SQL semantics and strong consistency. It is a specialized relational solution and therefore not the general NoSQL class the question asks for even though it can handle some of the scale and consistency needs of these applications.

When a question mentions high ingest, evolving schema, or massive horizontal scale look for answers that emphasize schema flexibility, horizontal scaling, and low latency to identify NoSQL choices.

Cloud Storage objects is the correct option for where raw unprocessed data is typically stored in a lakehouse pattern.

Cloud Storage objects provide durable, scalable, and low cost object storage for raw files such as logs, CSV, JSON, Avro, or Parquet and they serve as the landing zone before pipelines clean or transform the data. This storage integrates with services like Dataflow, Dataproc, and Dataplex which read the raw objects to perform ingestion, cleansing, and format conversions. Keeping raw data in object storage preserves the original data and supports lifecycle rules and partitioning for downstream ETL and analytics.

BigQuery tables are not the usual place to land raw unprocessed files. BigQuery is a managed analytical data warehouse that is optimized for fast queries and for storing processed or modeled datasets rather than acting as a file landing zone. Engineers typically load cleaned or transformed data into BigQuery for analysis.

Cloud Pub/Sub messages are a messaging system for streaming ingestion and decoupling producers from consumers. Pub/Sub is used to transmit events or records in motion and it is not intended to be a durable file store for raw datasets. Persistent raw storage is normally handled by object storage.

When a question asks where raw files are landed think object storage. Choose Cloud Storage for file based raw data and reserve BigQuery for processed queryable tables and Pub/Sub for streaming events.

Azure Data Lake Storage Gen2 is correct.

Azure Data Lake Storage Gen2 provides a hierarchical namespace and supports POSIX style access control lists that can be applied to individual files and directories. This allows administrators to set read write and execute permissions on a per file and per directory basis similar to a traditional file system which is why it is the correct choice.

Azure Blob Storage is incorrect because it does not provide POSIX style ACLs on directories. Blob storage uses containers and blobs and you manage access with IAM roles and shared access signatures rather than filesystem ACLs on directories.

Google Cloud Storage is incorrect because it treats directories as virtual prefixes and not as real filesystem directories. While object level ACLs exist historically for objects you cannot apply POSIX style ACLs to true directories in the way a hierarchical file system supports.

Azure Cosmos DB is incorrect because it is a distributed NoSQL database and not a file system. It does not provide filesystem level ACLs for files and directories.

When you see a question about ACLs on directories think about services that offer a hierarchical namespace and POSIX style permissions and look for ADLS Gen2 or similar filesystem oriented storage.

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

The best action is Move the high usage database to its own single database outside the pool.

Moving the heavy database to its own single database gives it dedicated compute and storage so it cannot starve the other pooled databases of resources. After you move it you can size that single database to match its workload and keep the remaining databases in the pool to share resources cost effectively.

Increase the performance tier of the elastic pool might temporarily provide more headroom but it can become costly and it does not eliminate the risk that one database will dominate shared pool resources when its workload is persistently higher.

Keep the elastic pool unchanged because pooling automatically balances workloads is incorrect because pooling helps absorb variable usage but it does not automatically prevent a single, consistently high usage database from degrading the rest of the pool.

Remove the problematic database from service is not a suitable solution because it disrupts users and is unnecessary when isolating or resizing the database can resolve the contention.

Isolate a consistently heavy workload rather than only scaling the pool. On exam questions look for answers that prevent resource contention by moving or resizing the offending database.

The correct answer is Cassandra API.

The Cassandra API implements the Cassandra wire protocol and supports the Cassandra Query Language CQL so existing Cassandra drivers and tools can connect to Cosmos DB and issue CQL queries against containers exposed through that API.

Gremlin API is incorrect because it is the Cosmos DB graph API and it uses the Gremlin traversal language rather than CQL.

Cloud Bigtable is incorrect because it refers to a Google Cloud managed wide column NoSQL service and it is not an Azure Cosmos DB API so it does not provide CQL support.

SQL API is incorrect because it targets JSON document workloads and it exposes a SQL like query surface for documents rather than the Cassandra Query Language.

When a question asks which Cosmos DB API supports a specific query language map the language to the API by protocol and client support. For example think CQL with Cassandra and graph traversals with Gremlin.

To provide ACID transactional guarantees and dependable version history for files in a data lake is the correct option.

The Delta Lake storage layer implements a transaction log and metadata layer on top of object storage to provide ACID transactions. This means commits are atomic and readers see consistent snapshots even during concurrent writes, and the platform keeps a dependable history of file versions for time travel and reproducibility.

The layer also supports schema enforcement and evolution which helps prevent or detect bad data during write operations and makes it easier to roll back or reproduce past table states for analytics and ETL workflows.

Vertex AI Model Registry is incorrect because that service manages machine learning models and their metadata rather than providing transactional file storage or a versioned data lake.

To act as a distinct tier for long term archival storage is incorrect because a delta layer is focused on transactionality and versioning rather than being a low cost cold archive tier.

To automatically tune queries and build indexes for very large datasets is incorrect because automatic query tuning and indexing are responsibilities of query engines or specialized indexing tools rather than the core purpose of a Delta Lake storage layer.

When a question mentions ACID or version history for files think of a transaction log or lakehouse layer like Delta Lake instead of archival storage or model registries.

The correct answer is Power BI.

Power BI is designed specifically for creating scalable interactive dashboards and for exploring and visualizing business datasets. It offers rich visualizations, drag and drop report authoring, real time dashboards, and enterprise scale options such as Power BI Premium and embedding so organizations can serve many users and handle large datasets.

Azure Synapse Analytics is an integrated analytics platform for large scale data warehousing and big data processing and it focuses on data integration and complex analytical queries rather than end user interactive dashboarding. It can supply data to visualization tools but it is not the primary reporting service.

Azure Data Factory is a data integration and ETL service for moving and transforming data across systems and it is not intended for building interactive dashboards or visual reports.

Azure Machine Learning is focused on training, deploying, and managing machine learning models and model operationalization and it does not provide the core dashboarding and business visualization features that Power BI offers.

Match the service to its primary purpose and prefer Power BI when the question asks about interactive dashboards or business reporting. Other services are for data movement, analytics, or machine learning.

SQL Server and Infrastructure as a Service is correct. SQL Server and Infrastructure as a Service describes running full instances of SQL Server on cloud virtual machines so you avoid managing local server hardware while you still control the operating system and database installation.

With SQL Server and Infrastructure as a Service the cloud provider manages the physical hosts and underlying infrastructure and you manage the VM guest operating system and the SQL Server software. That responsibility split matches the infrastructure as a service deployment model.

Microsoft 365 and Platform as a Service is wrong because Microsoft 365 is a software as a service offering and not a platform that you install on virtual machines.

SQL Server and Platform as a Service is wrong because a platform as a service database is provided as a managed service such as Azure SQL Database and it does not require you to run full SQL Server instances on virtual machines.

Microsoft 365 and Desktop as a Service is wrong because Microsoft 365 is SaaS and Desktop as a Service provides hosted desktops rather than hosting full SQL Server instances on VMs.

When you read these questions identify who manages the operating system and the database. If you manage the OS and install SQL Server the answer is likely IaaS. If the provider manages the database it is likely PaaS.

The correct option is Yes, Yes, Yes.

Managed cloud SQL services routinely include automated backups and the ability to restore to a point in time within the configured retention period. This capability is part of the backup and recovery features that let you recover from user errors or data corruption by restoring to a specific time during the retention window.

These services also provide built in high availability with automatic failover and redundant replicas to reduce downtime. High availability is implemented by using standby instances or replicas and automated failover so that client traffic can continue when an instance or zone fails.

Many managed SQL offerings can integrate with an Advanced Threat Protection capability or equivalent managed threat detection and response services to surface suspicious activity and provide additional security controls. That integration is offered so you can detect anomalous behavior and respond to potential threats.

Yes, No, Yes is incorrect because it denies the built in high availability and automatic failover feature. Managed SQL services normally include HA so answering No for that statement makes the option wrong.

Yes, No, No is incorrect because it rejects both the high availability and the threat protection statements. Both of those are commonly supported by managed SQL offerings so this option does not match the expected capabilities.

When facing multi statement questions evaluate each statement on its own and verify it against documented managed service features. Pay attention to keywords like point in time restore, high availability, and advanced threat protection when you decide true or false.

Nonrelational databases are schema flexible and allow each record to include only the fields it needs is correct.

Nonrelational or NoSQL stores such as document and key value databases let individual records or documents vary in structure so each item can include only the fields it requires. This flexibility makes it easier to store sparse or evolving data models without altering a central schema and it supports denormalized designs that can improve read performance.

Nonrelational systems require strict data normalization to avoid duplicated information is incorrect because nonrelational systems often tolerate duplication and denormalization intentionally to optimize reads and to simplify schema evolution rather than enforcing strict normalization rules.

Nonrelational databases store data in fixed flat tables with uniform columns for every record is incorrect because that description matches traditional relational tables where every row conforms to the same set of columns and a fixed schema.

Nonrelational databases automatically provide full ACID transactions across multiple independent documents by default is incorrect because many nonrelational stores prioritize scalability and availability and either provide weaker consistency or limit transaction scope. Full multi document ACID guarantees are not a universal default for NoSQL systems.

Look for phrases that indicate schema flexibility or denormalization to identify NoSQL properties and always check the service documentation for the exact transaction guarantees.

All Azure questions come from my DP-900 Udemy course and certificationexams.pro

The correct answer is Pipeline trigger. It is the element in Azure Data Factory that causes a pipeline to run.

Triggers are Azure Data Factory resources that start pipelines based on schedules, events, or tumbling windows and they can also be started manually. When a trigger fires the service creates a pipeline run and the pipeline activities execute within that run.

Pipeline parameter is an input value that you pass to a pipeline or to activities and it does not by itself start a pipeline. Parameters make pipelines reusable and configurable at runtime but they are not initiators.

Control flow element refers to constructs and activities inside a pipeline such as If Condition or ForEach that manage the execution path during a run. These control flow elements do not launch a pipeline from an idle state.

Pipeline activity is an action performed inside a pipeline such as copying data or executing a stored procedure and activities execute as part of a pipeline run. Activities do not independently cause a pipeline to start.

When a question asks what starts a pipeline think trigger and distinguish it from runtime parts of a pipeline such as activities, control flow constructs, and parameters.

The correct answer is Normalization.

Normalization is the process used to reorganize relational data to eliminate redundancy and to split records into many narrow tables. It applies formal normal forms to move repeated attributes into related tables and to enforce keys and dependencies so that each fact is stored in one place.

The result of Normalization is reduced duplicate data and fewer update anomalies while queries may require more joins and the schema can become more complex to design and maintain.

Denormalization is incorrect because it is the opposite approach. Denormalization deliberately introduces redundancy and merges tables to improve read performance at the cost of duplicated data.

BigQuery is incorrect because it is a managed analytics data warehouse service on Google Cloud and not a schema design or data reorganization process.

Centralization is incorrect because it refers to consolidating systems or control and not to the specific relational database steps that remove redundancy and split records across tables.

When a question describes removing redundancy and splitting a table into many narrow tables think of Normalization and remember that Denormalization is the opposite trade off to improve read performance.

The correct answer is Azure Event Hubs.

Azure Event Hubs is a purpose built ingestion service for high volume streaming telemetry and logs and it scales with partitions and throughput units to handle large event rates with low latency. It provides durable capture to Blob or Data Lake for replay and batch analytics and it integrates directly with downstream real time processors such as Azure Stream Analytics, Azure Functions, and Azure Databricks so dashboards and alerts can be powered immediately.

Azure Event Hubs also supports consumer groups so multiple independent downstream consumers can read the same stream and it offers throughput controls and partitioning that are essential for predictable, high throughput log ingestion.

Azure Databricks is a data processing and analytics platform rather than an ingestion endpoint. Databricks commonly consumes streams from services like Azure Event Hubs to perform processing and analytics, so it is not the primary choice for ingesting high volume logs.

Azure IoT Hub is optimized for secure device to cloud messaging and device management. It is ideal for scenarios that require per device identity and bi directional communication, but it is not the general purpose, high throughput ingestion service for arbitrary application logs where Azure Event Hubs is preferred.

Azure Data Factory is focused on orchestration and batch ETL and it does not provide the same low latency, high throughput streaming ingestion capabilities required for real time dashboards and alerts. Data Factory is better suited for scheduled or batch data movement and transformation.

Focus on the service purpose and required throughput when answering streaming questions. If the scenario needs continuous, low latency ingestion for many events choose Azure Event Hubs and if the scenario centers on physical devices and device management consider Azure IoT Hub.

The correct answer is Yes, Contoso Cloud provides SQL Server 2019 and 2022 images for Linux.

This is correct because SQL Server has been officially supported on Linux since SQL Server 2017 and vendors commonly publish ready to use VM or container images. Contoso Cloud providing SQL Server 2019 and 2022 images for Linux means you can deploy those images to a Linux host and run SQL Server natively on that Linux system.

No, SQL Server runs only on Windows Server is incorrect because Microsoft added Linux support starting with SQL Server 2017 and continued support for Linux in later releases including 2019 and 2022. SQL Server is not limited to Windows Server.

Use Cloud SQL for SQL Server as a managed hosted option is incorrect for this question because that option describes a managed database service rather than deploying SQL Server directly on a Linux host from an image. The question asks about deploying on a Linux host using Contoso Cloud images so the managed service option does not answer the deployment scenario.

Read each option carefully and watch for the words images versus managed. Images mean you can install or run the database on your VM, while managed means the provider hosts it for you. Also remember that SQL Server has supported Linux since 2017.

A destination data platform that can execute transformations after data is loaded is the correct option.

This statement describes the ELT pattern where data is extracted and loaded first and then transformed inside the destination system. The destination must be able to run transformations such as SQL queries, joins, aggregations, user defined functions, or stored procedures at scale for an extract load transform implementation to work correctly.

Dataflow is incorrect because it is a stream and batch processing service that can perform transformations during movement but it is not a required capability for the ELT pattern. The ELT approach depends on the destination being able to transform data after loading rather than on a particular processing tool.

Source data that is completely transformed before loading into the destination is incorrect because that describes a traditional ETL workflow where transformations happen before load. An extract load transform implementation specifically relies on doing transformations after the data is loaded into the destination.

An orchestration workflow that moves data and invokes transformations in separate tools is incorrect because orchestration can coordinate steps but it is not the essential capability that makes ELT possible. The key requirement is that the destination itself can execute the transformations once the data is loaded.

When the question contrasts extract load transform with other patterns look for wording about where transformations occur. If the destination performs transformations the pattern is ELT and the destination must support in place transforms.

The correct option is Mask sensitive fields by replacing them with realistic non-sensitive values to keep datasets usable for testing.

Mask sensitive fields by replacing them with realistic non-sensitive values to keep datasets usable for testing is the primary purpose because it removes or alters personally identifying values while preserving format and realism so developers and testers can run scenarios without exposing customer identifiers. Masking preserves data formats and referential integrity so tests remain meaningful while sensitive values are replaced with plausible non sensitive ones.

Compress datasets to lower storage requirements is wrong because compression only reduces storage size and does not hide or replace sensitive values so it does not protect customer identifiers for testing.

Google Cloud Data Loss Prevention is wrong because that is a Google Cloud product rather than an Azure feature and the question is about masking in an Azure data platform. It is also a different tool even though it can classify and transform data in Google Cloud.

Encrypt data at rest and in transit for stronger protection is wrong because encryption protects confidentiality while data is stored or moving and it does not produce realistic, non sensitive test values. Encrypted data is not directly usable for normal testing without decryption which defeats the purpose of creating safe test datasets.

When a question asks about making realistic test data while protecting identifiers look for answers that mention masking or redaction rather than encryption or compression. Also confirm the cloud platform named to rule out cross cloud services.