DP-900 Microsoft Data Fundamentals Practice Exams

What is a key advantage of choosing the serverless billing option in Azure Cosmos DB for an application that experiences intermittent traffic?

• ❏ A. Predictable monthly billing

• ❏ B. Google Cloud Spanner

• ❏ C. Lower cost for infrequent or bursty usage

• ❏ D. Automatic expansion of stored data

Fill in the blank for the Contoso cloud scenario. A(n) [?] helps organizations get the most value from their data assets. They design and build scalable data models, clean and transform datasets, and enable advanced analytics through dashboards and visual reports. A(n) [?] converts raw data into actionable findings based on the business needs identified to produce useful insights?

• ❏ A. Cloud Database Administrator

• ❏ B. Cloud Data Analyst

• ❏ C. BigQuery Data Analyst

• ❏ D. Cloud Data Engineer

Which sign in method should be used to require multi factor authentication for users who connect to an Azure SQL database?

• ❏ A. Certificate based authentication

• ❏ B. Service principal authentication

• ❏ C. SQL authentication

• ❏ D. Microsoft Entra authentication

Identify the missing term in this sentence about Microsoft Azure Tables service where the service uses [?] to group related entities by a shared key so that entities with the same key are stored together and this technique also helps organize data and enhance scalability and performance?

• ❏ A. Cloud Bigtable

• ❏ B. Sharding

• ❏ C. Partitioning

• ❏ D. Inner joining

A data team at Meridian Systems uses a table based storage service for large scale key value records and they need to know the upper storage limit for a single account. What is the maximum amount of data Meridian Systems can store in one table storage account?

• ❏ A. 500 TB

• ❏ B. Cloud Bigtable

• ❏ C. Unlimited

• ❏ D. 5 PB

Which term best completes this description for a cloud operations dashboard at Fabrikam Cloud where metrics are extracted from the underlying IT systems as numbers statistics and activity counts then processed by specialized software and presented on the dashboard?

• ❏ A. Data querying

• ❏ B. Cloud Monitoring

• ❏ C. Data visualization

• ❏ D. Data analytics

• ❏ E. Data transformation

In the Microsoft Azure context which term fills the blank in this sentence? [?] grants temporary permissions to items inside an Azure storage account so that applications can access blobs and files without first being authenticated and it should only be used for content you intend to expose publicly?

• ❏ A. SAML

• ❏ B. Cloud Storage Signed URL

• ❏ C. Shared Access Signature

• ❏ D. SSL

Contoso Cloud offers a platform that hosts enterprise applications and IT infrastructure for many large organizations and it includes services for both transactional and analytical data workloads. Which service delivers a fully managed relational database with near one hundred percent feature compatibility with Microsoft SQL Server?

• ❏ A. Azure Synapse Analytics

• ❏ B. SQL Server on Azure Virtual Machines

• ❏ C. Azure SQL Database

• ❏ D. Azure SQL Managed Instance

Which cloud service supports interactive data exploration visualization and collaborative report creation?

• ❏ A. Azure HDInsight

• ❏ B. Azure Data Factory

• ❏ C. Power BI

• ❏ D. Azure Analysis Services

A cloud team at NebulaApps manages a Cosmos DB account named StoreAccount42 that uses two primary keys for administration and data access and one of the primary keys was unintentionally shown in a public screencast with no evidence of misuse so far What immediate action should you take?

• ❏ A. Regenerate the exposed primary key only

• ❏ B. Create a fresh Cosmos DB account and move all data to it

• ❏ C. Switch applications to the secondary primary key and then regenerate the exposed primary key

• ❏ D. Apply Azure role based access control to grant minimal permissions instead of using primary keys

A data engineering team at Meridian Analytics needs a storage account configuration that allows them to apply access controls at the folder level and to perform directory operations atomically, which setting should they enable?

• ❏ A. Enable role based access control at the account level

• ❏ B. Configure replication to read access geo redundant storage RA GRS

• ❏ C. Enable hierarchical namespace on the storage account

• ❏ D. Change the storage account type to BlobStorage

Fill in the blank in this sentence for NimbusCloud analytics tools. [?] helps you quickly detect patterns anomalies and operational problems and it lets you focus on the meaning of information rather than inspecting raw records?

• ❏ A. BigQuery

• ❏ B. Data reconciliation

• ❏ C. Data visualization

• ❏ D. Data forecasting

A boutique analytics firm named Harbor Insights records click events in an Azure SQL table called WebHits with columns VisitorID, URLPath and EventTime. You need to determine which page paths receive the most visits. Which T SQL statement will return each page and the number of hits it received?

• ❏ A. SELECT URLPath, COUNT(DISTINCT VisitorID) AS UniqueVisitors FROM WebHits GROUP BY URLPath

• ❏ B. SELECT DISTINCT URLPath FROM WebHits

• ❏ C. SELECT URLPath FROM WebHits WHERE EventTime = (SELECT MAX(EventTime) FROM WebHits)

• ❏ D. SELECT URLPath, COUNT(*) AS PageViews FROM WebHits GROUP BY URLPath

A small analytics team at NovaData needs to provision a NoSQL database just once for a proof of concept. Which provisioning method is most appropriate for a single one off creation of the database?

• ❏ A. Bicep script

• ❏ B. Google Cloud Console

• ❏ C. ARM template deployment

• ❏ D. Azure Portal

Within the context of the Contoso Cloud platform which term completes the sentence “general purpose file storage for binary large objects fits any scenario”?

• ❏ A. Azure File Storage

• ❏ B. Azure Disk Storage

• ❏ C. Azure Queue Storage

• ❏ D. Azure Blob Storage

A data engineering group at Meridian Insights needs to process and query very large graph datasets to uncover relationships and perform complex traversals. Which Azure service is best suited for that type of workload?

• ❏ A. Azure Databricks

• ❏ B. Azure Cosmos DB Gremlin API

• ❏ C. Azure Synapse Analytics

• ❏ D. Azure SQL Database

A data engineer at NorthStar Analytics must complete a description of a column oriented storage format. A(n) [?] is a columnar data format. It was developed by DataForge and SocialStream. A [?] file holds row groups. Data for each column is stored together within each row group. Each row group contains one or more chunks of data. A [?] file contains metadata that describes the row ranges inside each chunk. How should the blank be filled?

• ❏ A. ORC

• ❏ B. CSV

• ❏ C. BigQuery

• ❏ D. JSON

• ❏ E. Parquet

• ❏ F. Avro

• ❏ G. XLSX

At Contoso Data Pipelines which activities are classified as part of the orchestration control flow rather than the data transformation layer?

• ❏ A. Cloud Dataflow

• ❏ B. Mapping data flow

• ❏ C. If condition activity

• ❏ D. Copy activity

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

A payments company named HarborPay runs a real time transaction system that handles many concurrent writes and reads and requires very low latency and high throughput. Which Azure SQL Database service tier best fits this workload?

• ❏ A. General Purpose

• ❏ B. Business Critical

• ❏ C. Hyperscale

• ❏ D. Basic

A mobile game studio must capture live telemetry from players to make immediate adjustments and monitor events as they happen. Which class of workload best fits this scenario?

• ❏ A. Online transaction processing (OLTP)

• ❏ B. Cloud Pub/Sub

• ❏ C. Streaming

• ❏ D. Batch processing

Which database model matches these properties All data is arranged in tables and entities are represented by tables with each record stored as a row and each attribute stored as a column All rows in the same table share the same set of columns Tables can contain any number of rows A primary key uniquely identifies each record so that no two rows share the same primary key and a foreign key refers to rows in a different related table which model fits this description?

• ❏ A. Cloud Bigtable

• ❏ B. NoSQL database

• ❏ C. Firebase Realtime Database

• ❏ D. Relational database

Your team manages an Acme document database that exposes a MongoDB compatible interface and you need to retrieve every record that contains a particular key. Which MongoDB query operator should you use?

• ❏ A. $in

• ❏ B. Google Firestore

• ❏ C. $type

• ❏ D. $exists

Which description most accurately defines a stored procedure in a relational database management system?

• ❏ A. A serverless function executed in Cloud Functions

• ❏ B. A virtual table whose rows are produced by a query

• ❏ C. A collection of one or more SQL statements stored on the database server and invoked with parameters

• ❏ D. A schema object that stores all of the data in the database

A retail analytics team requires an analytical store that presents processed structured data for reporting and supports queries over both near real time streams and archived cold data. These analytical stores are part of the serving layer. Which service is implemented on Apache Spark and is available across multiple cloud providers?

• ❏ A. Google Cloud Dataproc

• ❏ B. Azure Data Factory

• ❏ C. Azure Synapse Analytics

• ❏ D. Databricks

How do “structured” and “semi-structured” data differ in their organization and validation for a cloud analytics team at Nimbus Analytics?

• ❏ A. Structured data is stored exclusively in relational databases and semi-structured data is only saved in object storage

• ❏ B. Structured data requires a predefined schema while semi-structured data contains self-describing fields and flexible structure

• ❏ C. Semi-structured data is easier to scale for large analytics workloads compared with structured data

• ❏ D. Structured data is always more secure than semi-structured data

How many databases can you provision inside a single Azure Cosmos account at present?

• ❏ A. 50 databases

• ❏ B. 250 databases

• ❏ C. No upper limit

• ❏ D. 1 database

A logistics startup called Meadowlane collects large volumes of timestamped telemetry from its delivery drones and needs a platform to ingest store and explore time series sensor records at scale. Which Azure service is most appropriate for this workload?

• ❏ A. Azure Cosmos DB

• ❏ B. Azure Blob Storage

• ❏ C. Azure Time Series Insights

• ❏ D. Azure SQL Database

Meridian Shop is a regional e commerce firm that is observing a 75 percent increase in read requests against a single Azure SQL database which is causing sluggish response times. You need to divert a portion of the read workload away from the primary database to reduce latency and increase throughput. Which Azure SQL Database capability should you use to accomplish this?

• ❏ A. Columnstore indexes

• ❏ B. Elastic pools

• ❏ C. Read replicas

• ❏ D. Active geo replication

A regional retailer keeps its operational records in tables that use named fields and rows to relate information. Which storage model uses a rows and columns layout to make retrieval and querying efficient?

• ❏ A. Plain text file

• ❏ B. Spreadsheet

• ❏ C. Relational database

• ❏ D. Document database

In the context of a cloud data fundamentals course for Acme Cloud what term completes the sentence ‘[?] is a type of data that does not conform to a strict tabular schema because each record may include a different set of attributes and it is a less formally structured kind of information’?

• ❏ A. Unstructured data

• ❏ B. Semi structured data

• ❏ C. NoSQL document data

• ❏ D. Structured data

Identify the missing word or words in the following statement about Contoso Cloud. [?] is a scalable and fault tolerant platform for running real time data processing applications. [?] can process high volumes of streaming data using comparatively modest compute resources. [?] is designed for reliability so that events are not lost. Its solutions can provide guaranteed processing of data and they allow replaying data that failed initial processing. [?] can interoperate with a range of event sources including Contoso Event Streams, Contoso IoT Hub, Apache Kafka, and RabbitMQ?

• ❏ A. Apache Flink

• ❏ B. Databricks

• ❏ C. Apache Storm

• ❏ D. Apache Spark

What function does indexing serve in Nimbus cloud data storage systems?

• ❏ A. To create redundant copies of data for disaster recovery

• ❏ B. To secure stored information by applying encryption at rest

• ❏ C. To accelerate query performance by building a structure for fast lookups

• ❏ D. To reduce the storage footprint by compressing data

You operate a live order processing system for a retail company named HarborGrocer and the application performs reads and writes against an Azure SQL Database at roughly 120 operations per second. Occasionally the application raises an exception that shows “40501 The service is currently busy.” What is the most likely cause of this error and how should you mitigate it?

• ❏ A. Add client side retry logic with exponential backoff and use connection pooling in the application

• ❏ B. Move the database to a dedicated single tenant instance or migrate it to SQL Server on a virtual machine

• ❏ C. Scale the database to a higher DTU or vCore service tier to increase IOPS capacity

• ❏ D. Open a support case to ask Microsoft to raise the IOPS limits for the current service tier

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

Identify the missing term in the following sentence about cloud data platforms. is a field of technology that helps with extraction processing and analysis of information that is too large or complex for conventional software?

• ❏ A. Unstructured data

• ❏ B. Semi-structured data

• ❏ C. Big Data

• ❏ D. Structured data

If a transactional relational database at a retail analytics firm faces very high transaction volumes and performance degrades what common but difficult technique is used to spread those transactions across multiple servers?

• ❏ A. Cloud SQL read replicas

• ❏ B. Vertical scaling

• ❏ C. Manual data sharding

• ❏ D. Cloud Spanner

Which category of database management systems do PostgreSQL MariaDB and MySQL belong to?

• ❏ A. Cloud Spanner

• ❏ B. Relational database management systems that use SQL

• ❏ C. Non relational database management systems

• ❏ D. Hybrid database management systems

A regional payments provider runs a managed SQL instance that must serve many simultaneous users and services that update the same records. Which concurrency approach should the team choose to keep data consistent and prevent transaction conflicts?

• ❏ A. Exclusive locks

• ❏ B. Snapshot isolation

• ❏ C. Optimistic concurrency control

• ❏ D. Pessimistic concurrency control

A retail analytics team at Meridian Logistics must process live telemetry and clickstream events with very high throughput and minimal delay for on the spot computations and routing. Which Azure service is designed for ingesting and analyzing real time event streams?

• ❏ A. Azure Event Hubs

• ❏ B. Azure Synapse Analytics

• ❏ C. Azure Cosmos DB

• ❏ D. Azure Stream Analytics

How would you describe the phenomenon called “data drift” in machine learning and why should engineering teams be worried about it?

• ❏ A. Changes in the relationship between features and target labels over time

• ❏ B. Model performance deterioration caused by shifts in the input data distribution

• ❏ C. Vertex AI Model Monitoring

• ❏ D. The reformatting and cleaning of raw inputs so they are suitable for training

A retail analytics team at Bluewave Analytics uses Azure Cosmos DB for telemetry storage. At which two scopes can they configure provisioned throughput for their Cosmos DB resources? (Choose 2)

• ❏ A. Partition

• ❏ B. Container

• ❏ C. Item

• ❏ D. Database

A healthcare analytics team at Meridian Insights uses Azure Synapse to process very large datasets. The dedicated SQL pool in Synapse can be manually scaled up to [?] nodes and it can be paused to release compute resources while it is not needed which stops billing for those compute resources until the pool is resumed. Which number completes the sentence?

• ❏ A. 30

• ❏ B. 125

• ❏ C. 75

• ❏ D. 15

• ❏ E. 180

• ❏ F. 45

A retail analytics team at Solstice Retail needs guidance on whether to use a data lake or a data warehouse and they ask what the fundamental distinction is between “data lake” and “data warehouse”?

• ❏ A. Google Cloud Storage and BigQuery

• ❏ B. Data lakes hold raw unprocessed data while data warehouses hold structured curated datasets prepared for reporting

• ❏ C. Data lakes are optimized for real time analytics and data warehouses handle only batch workloads

• ❏ D. Data lakes store only unstructured files while data warehouses accept only tabular structured data

A data team at Nimbus Analytics can still reach several services in their Azure subscription after their office public IP changed but they cannot connect to their Azure SQL database from the internet. What could explain why the database is unreachable while other Azure resources remain accessible?

• ❏ A. Network Security Group

• ❏ B. Azure Private Endpoint configuration

• ❏ C. Azure SQL server firewall rule

• ❏ D. Azure Role Based Access Control

In Azure Cosmos DB what is the purpose of the Time to Live setting and how do teams usually apply it?

• ❏ A. Tuning queries and execution plans to lower latency

• ❏ B. Defining consistency guarantees for replicated data

• ❏ C. Setting automatic removal of items after a defined lifetime

• ❏ D. Configuring indexing behavior for containers

Which of the following is not a typical characteristic of relational databases at Contoso Cloud?

• ❏ A. Data integrity enforced through primary foreign and check constraints

• ❏ B. Cloud Spanner

• ❏ C. A flexible schema that allows frequent and easy restructuring of table rows

• ❏ D. Atomicity consistency isolation and durability guarantees for transactions

For a data ingestion pipeline that must grow to accommodate very large datasets which loading strategy is better suited for scalability and high volume ingestion?

• ❏ A. ETL

• ❏ B. ELT

For a Cosmos DB account that uses the Core SQL API what two container level configuration items can you change to affect performance and the way data is partitioned? (Choose 2)

• ❏ A. Read region

• ❏ B. Partition key

• ❏ C. Consistency level

• ❏ D. Provisioned throughput

True or False. Meridian Freight stores its transactional data in Azure SQL Database and Microsoft guarantees the databases will be available 100 percent of the time?

• ❏ A. False

• ❏ B. True

NorthField Devices operates a fleet of field sensors and needs a live dashboard to visualize incoming telemetry as it arrives in real time. Which Azure service is most appropriate for building that interactive dashboard?

• ❏ A. Azure Stream Analytics

• ❏ B. Azure Data Lake Storage

• ❏ C. Azure Synapse Analytics

• ❏ D. Azure Time Series Insights

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

An analytics team at CedarBridge receives transaction files from a supplier every 24 hours for reporting and trend analysis. Which category of data analytics workload is most suitable for processing this daily delivered dataset?

• ❏ A. Cloud Pub/Sub

• ❏ B. Scheduled batch processing

• ❏ C. Continuous stream processing

• ❏ D. Real time analytics

When there are no network restrictions configured what security mechanism does an OrionDB account require by default to accept API requests?

• ❏ A. Client TLS certificate

• ❏ B. Username and password

• ❏ C. A valid authorization token

• ❏ D. Google Cloud IAM

In the Contoso Cloud Synapse environment what capability does the feature named “PolyBase” provide for accessing and querying data?

• ❏ A. A method for compressing stored data to save space

• ❏ B. BigQuery

• ❏ C. A federation feature that lets you execute T-SQL queries against external data sources

• ❏ D. A real time ingestion pipeline for streaming IoT telemetry

Novatech Logistics plans to move its on site big data clusters to Azure and they currently run Hadoop Spark and HBase technologies so which Azure service should they evaluate to reduce migration work and allow a lift and shift approach?

• ❏ A. Azure Databricks

• ❏ B. Azure HDInsight

• ❏ C. Azure Synapse Analytics

• ❏ D. Azure Data Lake Storage Gen2

• ❏ E. Azure Blob Storage

What is the maximum storage capacity allowed for a single database in Acme Cloud SQL service?

• ❏ A. 120 TB

• ❏ B. Virtually unlimited in the Hyperscale tier

• ❏ C. 3 TB

• ❏ D. 1.2 PB

Which service offers a NoSQL key value store where each record is kept as a collection of properties and is located by a unique key within a cloud platform’s data offerings?

• ❏ A. Azure SQL Database

• ❏ B. Azure HDInsight Hadoop

• ❏ C. Azure Table Storage

• ❏ D. Google Cloud Bigtable

The correct option is Lower cost for infrequent or bursty usage.

Azure Cosmos DB serverless billing charges for request units consumed and for storage, so an application that has intermittent or bursty traffic only pays for the capacity it actually uses. This makes the billing model more cost effective for workloads that are idle much of the time and that only need capacity during short spikes.

Because you do not provision and pay for fixed RU per second with serverless billing, you avoid paying for idle throughput and you do not need to manage RU provisioning for unpredictable traffic patterns. That operational simplicity and the consumption based cost model are the main reasons the Lower cost for infrequent or bursty usage option is correct.

Predictable monthly billing is incorrect because serverless is consumption based and not intended to provide a fixed monthly charge. Predictable monthly billing would describe a provisioned throughput or reserved capacity model instead.

Google Cloud Spanner is incorrect because it is a different database service from another cloud provider and it is not an advantage of an Azure Cosmos DB billing option.

Automatic expansion of stored data is incorrect in this context because automatic storage growth is a general platform behavior and it is not the key billing advantage of the serverless option. The serverless choice is primarily about how you are charged for throughput and not about special storage expansion capabilities.

When you see wording about infrequent, bursty, or pay per use think of consumption based pricing and identify the option that highlights lower cost for sporadic traffic.

The correct answer is Cloud Data Analyst.

A Cloud Data Analyst is the role that designs and builds scalable data models and that cleans and transforms datasets to make them usable. A Cloud Data Analyst enables advanced analytics by producing dashboards and visual reports and converts raw data into actionable findings that align with business needs.

Cloud Database Administrator is incorrect because that role focuses on provisioning configuring and maintaining database systems and not on producing analytics or business reports.

BigQuery Data Analyst is incorrect because it refers to a specialization tied specifically to BigQuery and the question describes a general analyst role that spans designing models cleaning data and delivering insights across tools.

Cloud Data Engineer is incorrect because data engineers primarily build and operate data pipelines and infrastructure to move and prepare data rather than focusing on interpreting the data and producing dashboards and business findings.

Read the role descriptions and match the primary responsibilities to the task. Focus on words like design transform and visualize to identify analyst roles and watch for options that emphasize operations or infrastructure instead.

Microsoft Entra authentication is correct. Microsoft Entra authentication integrates Azure SQL with the Microsoft Entra ID identity platform so that user sign ins are processed by the identity provider and can be protected with multi factor authentication.

Microsoft Entra authentication works by using Azure AD based identities and Conditional Access policies to enforce requirements such as MFA for interactive user sign ins to Azure SQL. Configuring Azure SQL to use Entra authentication means the database trusts the identity platform and therefore honors MFA rules you apply there.

Microsoft Entra authentication is the current name for the identity service formerly referred to as Azure Active Directory, and exam questions may use either name depending on when the content was written.

Certificate based authentication is incorrect because Azure SQL does not rely on client certificates as the mechanism to require per user multi factor authentication for interactive user sign ins.

Service principal authentication is incorrect because service principals represent applications or non interactive identities and they are not subject to user MFA requirements which apply to human sign ins.

SQL authentication is incorrect because SQL authentication uses static database credentials and cannot enforce Microsoft Entra based MFA for user connections.

When a question asks about enforcing MFA for users think about the identity provider and features like Conditional Access rather than authentication methods that rely on static credentials.

Partitioning is correct. Azure Tables uses a PartitionKey to group related entities by a shared key so entities with the same key are stored together and this partitioning helps organize data and improve scalability and query performance.

Partitioning is the general term for dividing table data into segments so each partition can be stored and served efficiently across nodes. In Azure Tables the PartitionKey defines the partition and it works with the RowKey to provide unique identities and fast lookups within a partition.

Cloud Bigtable is a Google Cloud managed NoSQL wide column database and it is not the term used by Azure Tables. It is a product name and not the concept of grouping entities by key in Azure Table storage.

Sharding is a related concept that refers to distributing data across multiple shards or database instances and people sometimes use it interchangeably with partitioning. The Azure Tables service specifically uses the term partitioning and PartitionKey so sharding is not the precise answer here.

Inner joining is a relational database operation that combines rows from two tables based on matching columns and it does not describe grouping entities by a shared partition key in Azure Tables.

When a question mentions grouping by a shared key or a PartitionKey look for the term partitioning or the storage specific feature because product names and similar concepts are common distractors.

5 PB is correct. This value is the published maximum capacity for a single table storage account and it represents the upper storage limit you can place in one account for table service data.

The table service is part of a storage account and the account level capacity limit therefore governs how much table data you can store. The documented account limit is 5 PB and that is why this numeric option is the correct choice for the upper bound on table storage.

500 TB is incorrect because it is a lower figure than the actual account capacity limit and does not match the documented maximum for a table storage account.

Cloud Bigtable is incorrect because it is the name of a managed NoSQL database service and not a numeric storage limit. It is also a different vendor service and therefore it does not answer the question about an account capacity value.

Unlimited is incorrect because storage accounts have documented capacity limits and they are not unlimited. The correct limit is a finite maximum which is the 5 PB value given above.

When a question asks about capacity limits verify the exact service scope and consult the provider documentation and remember that storage accounts commonly have a fixed per account capacity such as 5 PB.

Data visualization is the correct answer.

Data visualization describes the presentation of numeric metrics and activity counts as charts, graphs, or dashboard panels so that operators can quickly see system health and trends. The question specifically mentions extracting metrics, processing them with specialized software, and presenting them on a dashboard which matches the idea of visualization.

Data querying refers to retrieving or filtering data from databases or APIs and it does not specifically imply converting metrics into visual charts for display on a dashboard.

Cloud Monitoring is a specific Google Cloud service for collecting metrics and providing dashboards and alerts, but the question asks for the general term that describes presenting processed metrics rather than the product name.

Data analytics focuses on examining data to find patterns and derive insights and it is broader than simply showing numbers as visual elements on a dashboard. Analytics may feed visualizations but it is not the act of presenting them.

Data transformation covers cleaning, reshaping, and converting data formats and it describes processing steps rather than the graphical presentation of results.

When a question mentions charts, dashboards, or graphical presentation choose the term that emphasizes visualization rather than collection, transformation, or analysis.

The correct answer is Shared Access Signature.

A Shared Access Signature grants temporary and scoped permissions to specific resources inside an Azure storage account so applications can access blobs and files without requiring full authentication. A Shared Access Signature token can restrict operations and include start and expiry times so it is intended for content you choose to expose publicly for a limited period.

SAML is an authentication and federation protocol used for single sign on and identity exchange. It does not provide object level, time limited access tokens for Azure Storage so it is not the correct choice.

Cloud Storage Signed URL is a Google Cloud term for signed URLs that grant temporary access to storage objects. It is similar in function but it is not the Azure term, so it is incorrect in the Microsoft Azure context.

SSL refers to encrypting data in transit and securing connections. It does not grant permissions to storage resources or create temporary access tokens, so it does not match the description.

When a question mentions temporary or scoped access to Azure storage think of Shared Access Signature and watch for platform specific phrases like Cloud Storage Signed URL that indicate another cloud provider.

The correct answer is Azure SQL Managed Instance.

Azure SQL Managed Instance is a platform as a service offering that provides a fully managed relational database engine with near one hundred percent feature compatibility with Microsoft SQL Server. It supports instance scoped features such as SQL Agent, cross database transactions, and other capabilities that make lift and shift migrations from on premise SQL Server straightforward while Azure handles patching, backups, high availability, and maintenance.

Managed Instance is designed for enterprise applications that require full SQL Server feature parity and minimal changes to existing databases and administrative processes when moving to Azure.

Azure SQL Database is a managed database service but it focuses on single databases and elastic pools and it does not provide full instance level compatibility with SQL Server. Some server level features and instance scoped functionality are not available in that model.

SQL Server on Azure Virtual Machines gives you full compatibility because it runs the full SQL Server stack on an IaaS VM, but it is not a fully managed PaaS offering. You must manage the operating system, patching, and many administrative tasks yourself.

Azure Synapse Analytics is an analytics and data warehousing service optimized for large scale analytical workloads and integrated analytics. It is not intended to be a fully managed OLTP relational database with near one hundred percent SQL Server feature compatibility.

When a question asks for a fully managed service with near 100% SQL Server compatibility pick the Azure SQL option that explicitly advertises instance level parity and lift and shift migration support.

The correct answer is Power BI.

Power BI is a managed Microsoft service built for interactive data exploration and visualization and it supports collaborative report creation through the Power BI service where users can share workspaces, publish apps, and comment on reports. The platform includes authoring tools and cloud hosting so teams can iterate on visuals and dashboards together and share insights with stakeholders.

Azure HDInsight is a managed big data platform for Hadoop, Spark, and Kafka and it is focused on processing and running analytical workloads rather than providing an integrated interactive visualization and collaborative reporting environment.

Azure Data Factory is a data integration and orchestration service used to build ETL and ELT pipelines and it does not offer end user visualization or collaborative report authoring capabilities.

Azure Analysis Services provides enterprise semantic models and analytical processing similar to SQL Server Analysis Services and it is used to model and serve data to reporting tools but it does not itself provide the interactive visualization and collaborative report creation features of Power BI.

When a question mentions both interactive visualization and collaborative report creation choose a managed BI and reporting service such as Power BI rather than a data processing or orchestration service.

The correct answer is to Switch applications to the secondary primary key and then regenerate the exposed primary key.

You should first move application usage to the other primary key so that client connections remain functional and there is no service interruption. After the applications are using the secondary key you can safely regenerate the exposed primary key which immediately invalidates the leaked credential.

Azure Cosmos DB supports two primary keys so you can perform a seamless key rotation. This approach lets you revoke the compromised key right away while preserving availability by using the alternate key during the rotation.

Regenerate the exposed primary key only is not the best immediate action because regenerating the exposed key while applications still use it will cause outages. You should ensure clients switch to the alternate key before invalidating a key.

Create a fresh Cosmos DB account and move all data to it is an overly disruptive and time consuming step for an incident where a key has been exposed but there is no evidence of misuse. Key rotation is faster and avoids the complexity of data migration.

Apply Azure role based access control to grant minimal permissions instead of using primary keys is a good long term security improvement but it is not the quickest remediation for an immediately exposed primary key. Implementing RBAC can take planning and configuration while key rotation provides an immediate revocation of the leaked secret.

When a secret is exposed act quickly to rotate the credential while keeping services available. Use the two key pattern to switch clients to the alternate key before regenerating the compromised key.

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

Enable hierarchical namespace on the storage account is correct.

Enable hierarchical namespace on the storage account turns on Azure Data Lake Storage Gen2 features which include POSIX style access control lists on directories and files and support for atomic directory and file operations. This feature therefore allows the team to apply access controls at the folder level and to perform directory operations atomically as the question requires.

Enable role based access control at the account level is incorrect because RBAC governs management and resource level permissions and does not provide POSIX style folder level ACLs or atomic directory semantics that are required for directory operations.

Configure replication to read access geo redundant storage RA GRS is incorrect because replication settings control data durability and cross region read access and they do not enable folder level access controls or atomic directory operations.

Change the storage account type to BlobStorage is incorrect because the BlobStorage account kind by itself does not enable the hierarchical namespace features needed for folder ACLs and atomic directory operations. ADLS Gen2 functionality requires the hierarchical namespace capability on a compatible storage account.

When a question asks for folder level ACLs and atomic directory operations look for hierarchical namespace or Azure Data Lake Storage Gen2 in the options.

Data visualization is correct because it helps you quickly detect patterns anomalies and operational problems and it lets you focus on the meaning of information rather than inspecting raw records.

Data visualization uses charts graphs and dashboards to reveal trends clusters outliers and relationships in data so that teams can understand issues at a glance and act faster. Visualizations abstract raw records into visual forms which makes it easier to spot anomalies and operational problems without reading individual rows.

BigQuery is a scalable data warehouse for storing and querying large datasets and it is not itself a visualization tool even though it can feed data to visualization systems.

Data reconciliation is the process of comparing and correcting data between sources and it focuses on data quality rather than on presenting patterns visually.

Data forecasting creates models to predict future values and trends and it is about generating predictions rather than helping users visually inspect current records for patterns or operational issues.

When a question highlights seeing patterns or focusing on the meaning look for answers about visualization or dashboards rather than storage or modeling tools.

SELECT URLPath, COUNT() AS PageViews FROM WebHits GROUP BY URLPath* is correct.

This statement groups the rows by URLPath and uses COUNT(*) to count every hit for each path. That returns each page path together with the total number of page views which is exactly what is needed to determine which pages receive the most visits.

SELECT URLPath, COUNT(DISTINCT VisitorID) AS UniqueVisitors FROM WebHits GROUP BY URLPath is incorrect because it counts unique visitors per path rather than total hits. That produces a count of distinct users and not the number of page views.

SELECT DISTINCT URLPath FROM WebHits is incorrect because it only lists the unique URL paths and does not provide any counts. There is no aggregation so you cannot tell which pages have more visits from that result.

SELECT URLPath FROM WebHits WHERE EventTime = (SELECT MAX(EventTime) FROM WebHits) is incorrect because it returns only rows that match the latest event time. That yields recent events only and does not aggregate or count visits across all events.

When a question asks for the number of visits per category use COUNT(*) together with GROUP BY unless the question specifically asks for unique users or distinct counts.

The correct option is Azure Portal.

Using the Azure Portal is the simplest approach for a single one off proof of concept because it gives an interactive graphical interface that lets the team create a NoSQL database quickly without writing infrastructure code.

The portal provides guided forms, sensible defaults, and quickstart workflows so the team can focus on testing data and queries rather than building and validating deployment scripts.

Bicep script is an infrastructure as code tool intended for repeatable, automated deployments and pipeline integration. It requires authoring templates and using tooling which is unnecessary overhead for a one time manual proof of concept unless you specifically need the deployment described as code.

ARM template deployment is a declarative, repeatable deployment method that also requires template development and validation. It is better suited for consistent, version controlled deployments rather than a single quick creation for a proof of concept.

Google Cloud Console is the management interface for Google Cloud Platform and not for Azure. It cannot be used to provision Azure NoSQL databases so it is not applicable to this scenario.

When the scenario describes a single, one off resource creation choose the interactive portal unless the question explicitly asks for automation, repeatability, or infrastructure as code.

The correct answer is Azure Blob Storage.

It is the object storage service designed for large amounts of unstructured data and for binary large objects. It supports scenarios such as serving images and videos, backups and archives, and analytics data lakes, which makes it the natural fit for the phrase general purpose file storage for binary large objects.

Azure File Storage provides managed file shares accessible over SMB or NFS and is intended for lift and shift scenarios and applications that require file system semantics. It is not an object store optimized for large unstructured blobs.

Azure Disk Storage is block storage that attaches to virtual machines for persistent OS and data disks. It is designed for VM workloads and not for general purpose blob storage.

Azure Queue Storage is a messaging service used to decouple application components and to pass messages between services. It is not meant for storing binary large objects.

When a question mentions binary large objects or unstructured data prefer the object storage service. Scan options for words like file share or attached disk to rule out other storage types.

Azure Cosmos DB Gremlin API is the correct option for processing and querying very large graph datasets to uncover relationships and perform complex traversals.

The Azure Cosmos DB Gremlin API is a managed graph database that implements the Apache TinkerPop Gremlin traversal language and it is designed for expressing complex multi hop traversals and relationship queries. It provides horizontal scale and low latency reads which helps when working with very large graphs, and it integrates with Cosmos DB features for global distribution and flexible consistency models to support large scale graph workloads.

Azure Databricks offers powerful distributed processing and can analyze graph data with additional libraries, but it is not a native graph database and it is less suited for interactive low latency graph traversals than a Gremlin API optimized store.

Azure Synapse Analytics is focused on distributed analytics and data warehousing and it can process large datasets with SQL and Spark, but it is not specialized for relationship centric traversals the way a Gremlin based graph service is.

Azure SQL Database is a relational engine that can represent relationships with tables and joins, but it lacks native Gremlin traversal support and it does not provide the traversal performance characteristics of a purpose built graph database.

When a question emphasizes multi hop traversals and relationship queries look for services that natively support a graph model and the Gremlin traversal language rather than general analytics or relational engines.

The correct answer is Parquet.

Parquet is a columnar storage file format that organizes data into row groups and stores all values for each column together within each row group. Each row group contains one or more column chunks and those chunks are further divided into pages. A Parquet file also includes footer metadata that describes the row ranges and page indexes so readers can skip data and read only the needed columns and row ranges.

ORC is also a columnar format but it uses a stripe based layout and different metadata structures so the specific description in the question matches Parquet more closely.

CSV is a plain text, row oriented format with no column chunks or file footer metadata describing row ranges.

BigQuery is a managed analytics service and not a file format that contains row groups and column chunks.

JSON is a text serialization format for structured objects and it is not a columnar storage format with row groups.

Avro is a row oriented binary serialization format that includes schemas but it stores records row by row rather than by column chunks.

XLSX is a spreadsheet file format based on XML in a zipped container and it does not use Parquet style row groups and column chunks for efficient analytic reads.

When you see terms like row groups or column chunks look for a columnar file format on the answer list and eliminate row oriented formats first.

The correct answer is If condition activity.

If condition activity is an orchestration control flow construct because it evaluates a boolean expression and controls whether subsequent activities run. It is used for branching and conditional execution and it orchestrates the pipeline rather than performing data movement or transformation.

Cloud Dataflow is a managed service for stream and batch data processing and it belongs to the data transformation layer because it runs data processing jobs and transforms data rather than directing pipeline control flow.

Mapping data flow is a visual, scalable data transformation feature and it executes transformations on incoming data streams or batches. It is part of the transformation layer and not a control flow activity, so it does not orchestrate branching or conditional execution.

Copy activity is designed to move or ingest data between stores and it is a data movement or transformation task rather than an orchestration construct. It does not control pipeline branching or sequencing by itself.

When deciding between orchestration and transformation ask whether the activity controls execution such as branching or looping or whether it moves or transforms data. Activities that control flow are orchestration and activities that process or transfer data belong to the transformation layer.

The correct option is Business Critical.

The Business Critical tier is optimized for transactional workloads that need very low latency and high throughput. It places database files on local SSD storage and uses an Always On availability architecture with multiple replicas which reduces I/O latency and improves write and read performance for high concurrency systems.

The Business Critical tier also provides readable secondary replicas that can be used to offload read workloads and it supports high compute and in-memory options that help sustain many concurrent writes and reads while keeping response times low.

General Purpose is not ideal because it uses remote storage and is designed for balanced, cost efficient workloads that tolerate some additional I/O latency compared with local SSD storage.

Hyperscale is designed mainly for very large databases and fast scale of storage and backup operations. It is not primarily chosen for the lowest possible I/O latency for heavy, concurrent OLTP workloads.

Basic is intended for small development or light production workloads and it lacks the I/O throughput and concurrency capacity required for a real time, high throughput transaction system.

Look for keywords like low latency and high throughput and favor tiers that use local SSD storage and availability replicas when you need high OLTP performance.

The correct option is Streaming.

Streaming is the right workload class because live telemetry requires continuous ingestion and low latency processing so events can be analyzed and acted on as they occur. Streaming supports windowing and incremental aggregation which lets the studio adjust game parameters and monitor events in near real time.

To build a streaming pipeline you typically pair a messaging ingress with a stream processing engine. For example you might ingest events with Cloud Pub/Sub and process them with a streaming engine such as Dataflow or another stream processor to compute metrics and trigger immediate actions.

Online transaction processing (OLTP) is wrong because OLTP describes transactional database workloads that focus on many small, ACID protected reads and writes rather than continuous event stream analysis. OLTP systems are not designed for the low latency event aggregation and real time analytics required for live telemetry.

Cloud Pub/Sub is wrong as the answer because it is a messaging service and not a class of workload. It can be part of a streaming solution by handling event ingress but the workload class that describes continuous, low latency processing is streaming.

Batch processing is wrong because batch jobs collect and process data in large groups at scheduled intervals which introduces latency. This makes batch unsuitable when you need immediate adjustments and real time monitoring of player telemetry.

When a question asks about continuous low latency analytics think streaming and distinguish services that enable streaming from the workload class itself.

The correct answer is Relational database.

A Relational database arranges data in tables where each entity type is a table and each record is a row while each attribute is a column. The description of a primary key uniquely identifying each record and a foreign key referring to rows in a different related table matches the fundamental relational model and how SQL databases enforce relationships and integrity.

Cloud Bigtable is not correct because it is a wide column NoSQL store that uses sparse, schema flexible tables and does not enforce fixed columns or relational foreign key constraints. It is optimized for large scale and throughput rather than relational joins and referential integrity.

NoSQL database is not correct because that is a broad category that includes many nonrelational models. The properties in the question describe a fixed tabular schema with primary and foreign keys which is specific to relational systems and not a general characteristic of NoSQL databases.

Firebase Realtime Database is not correct because it stores data as a JSON tree for real time synchronization and does not use the table row and column structure nor enforce primary key and foreign key relationships in the way relational databases do.

Look for the words table, row, column, primary key, and foreign key in the question. Those terms almost always point to a relational database answer.

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

$exists is correct because it is the MongoDB query operator that checks for the presence of a field and so it is used to retrieve every record that contains a particular key.

The $exists operator matches documents where the specified field is present when set to true and it can be combined with other criteria to narrow results based on both presence and values.

$in is incorrect because it tests whether a field’s value equals any value in a given array and it does not check whether the key itself exists.

Google Firestore is incorrect because it names a different document database product and it is not a MongoDB query operator that you would use to test for a key in a MongoDB compatible interface.

$type is incorrect because it matches documents by the BSON type of a field and it does not solely test for the existence of the field.

When a question asks about whether a field is present look for operators that test for presence rather than value or type and remember that $exists checks presence while $in and $type do not.

The correct answer is A collection of one or more SQL statements stored on the database server and invoked with parameters.

A collection of one or more SQL statements stored on the database server and invoked with parameters describes a stored procedure because it bundles SQL and procedural logic on the database side and it is executed by the database engine when called. A stored procedure can accept input and output parameters and it runs under the database process so it can manage transactions, enforce permissions, and reduce client server round trips.

A serverless function executed in Cloud Functions is incorrect because Cloud Functions are external serverless compute resources and not SQL routines stored inside the database. They run in a cloud runtime and are managed separately from the database engine.

A virtual table whose rows are produced by a query is incorrect because that definition matches a view rather than a stored procedure. A view represents query results as a virtual table and it does not encapsulate procedural logic or accept invocation parameters in the same way.

A schema object that stores all of the data in the database is incorrect because tables and storage structures hold data and a schema is a namespace. No single schema object stores all data, and that description does not match the purpose of a stored procedure.

Read the wording carefully and look for phrases like stored on the database server and invoked with parameters as those clues usually point to a stored procedure.

The correct answer is Databricks.

Databricks is built on Apache Spark and is provided as a managed analytics platform across multiple cloud providers. It supports unified batch and streaming processing and integrates with Delta Lake and other components so analysts can run queries over near real time streams and archived cold data as part of a serving layer.

Google Cloud Dataproc is a managed Spark and Hadoop service but it is specific to Google Cloud and not a multi cloud Spark platform, so it does not match the requirement for availability across multiple cloud providers.

Azure Data Factory is primarily an orchestration and ETL service for moving and transforming data and it is not itself an analytical store implemented on Apache Spark for serving layer queries.

Azure Synapse Analytics is a comprehensive analytics service on Azure that can include Spark workloads, but it is an Azure native offering rather than a multi cloud managed Spark platform like the correct answer.

When the exam asks for a Spark based solution that runs across clouds look for products that explicitly advertise multi cloud support and Apache Spark implementation rather than services that are tied to a single cloud provider.

Structured data requires a predefined schema while semi-structured data contains self-describing fields and flexible structure.

This option is correct because structured data is defined by a fixed schema that is enforced when data is written and validated by systems like relational databases and data warehouses. Semi-structured data uses self describing formats such as JSON or XML so records can vary and the schema can be applied when the data is read.

Semi structured formats carry field names or tags with the data which makes them flexible for evolving sources and for ingesting varied records without changing a central schema. This flexibility is why teams often use schema on read for semi structured data and schema on write for structured data.

Structured data is stored exclusively in relational databases and semi-structured data is only saved in object storage is incorrect because storage is not exclusive to a format. Structured data can live in warehouses, columnar stores, or specialized databases and semi structured data can be stored in databases, document stores, or object storage depending on access and tooling.

Semi-structured data is easier to scale for large analytics workloads compared with structured data is incorrect because scalability depends on the chosen storage and processing architecture rather than the inherent format. Both structured and semi structured data can scale well when used with distributed systems and the right data engineering patterns.

Structured data is always more secure than semi-structured data is incorrect because security is determined by access controls, encryption, and governance policies rather than the data format. Both formats can be protected to strong security standards when proper controls are applied.

When you see choices about schemas focus on whether the schema is enforced at write time or whether the data is self describing and validated at read time. Remember that schema on write maps to structured data and schema on read maps to semi structured data.

The correct option is No upper limit.

No upper limit is correct because Azure Cosmos DB does not impose a fixed maximum number of databases per account. You can create as many databases as your application requires and practical constraints are driven by provisioned throughput, storage usage, and other account resource quotas rather than a hard count limit.

50 databases is incorrect because Cosmos DB does not restrict accounts to exactly fifty databases and that number is not a documented hard limit.

250 databases is incorrect because there is no published fixed cap at two hundred and fifty databases per account and the service scales based on resource and throughput considerations.

1 database is incorrect because an Azure Cosmos account can host multiple databases and it is not limited to a single database.

When questions ask about counts or quotas check the current documentation during study and prefer answers that reflect service scaling and resource limits rather than arbitrary fixed numbers.

Azure Time Series Insights is the correct choice for ingesting storing and exploring large volumes of timestamped telemetry from delivery drones at scale.

Azure Time Series Insights is a managed time series analytics platform that is purpose built for high volume telemetry. It provides scalable ingestion and storage optimized for time stamped records and it includes fast time based queries and built in visualization tools for interactive exploration of sensor data. It also integrates with Event Hubs and IoT Hub so you can stream telemetry directly into the service.

Azure Cosmos DB is a globally distributed NoSQL database that can store telemetry and it offers low latency and flexible schemas. It is not specialized for time series exploration and it lacks the built in time based analytics and visualization features that Time Series Insights provides.

Azure Blob Storage is object storage that is cost effective for raw log or telemetry dumps and it is suitable for long term archival. It does not provide native time series querying or interactive exploration without additional processing or services layered on top.

Azure SQL Database is a relational database that can model time stamped data and it can be used for analytics. Large scale high velocity telemetry workloads are better served by a purpose built time series service because that reduces operational overhead and provides faster time based insights.

When a question mentions large volumes of timestamped sensor telemetry look for a service that is purpose built for time series ingestion and exploration. For Azure that is Azure Time Series Insights.

The correct option is Read replicas.

Read replicas create one or more readable secondary databases that can serve read only queries so you can divert a portion of the read workload away from the primary database. This reduces latency and increases read throughput by distributing queries across the replicas rather than overloading the primary.

Read replicas are designed for read scale out and allow multiple replicas to handle read traffic while the primary focuses on writes, which directly addresses the scenario of a sudden increase in read requests.

The Columnstore indexes option is incorrect because columnstore indexes are an index structure that improves analytical query performance and storage compression on a single database. They do not create additional database instances or readable secondaries to offload read traffic.

The Elastic pools option is incorrect because elastic pools let multiple databases share resources to optimize cost and manage variable load. They do not provide replicas or a mechanism to distribute read queries across separate database instances.

The Active geo replication option is incorrect in this scenario because active geo replication is focused on business continuity and cross region failover and it creates readable secondaries primarily for disaster recovery. While those secondaries can be used for reads they are not intended as the primary mechanism for local read scale out and they can introduce higher replication lag and cross region latency.

When a question asks about offloading read traffic focus on features that create readable secondaries. Look for language that emphasizes scale out for reads or read replicas rather than disaster recovery or indexing features.

The correct option is Relational database.

Relational database systems organize data into tables that use named columns and rows so queries can reference fields directly and return results efficiently. They provide a fixed schema, indexing, and SQL query engines that make retrieval and joining of related records fast and predictable.

Relational database designs also support constraints and transactions which help maintain data integrity when multiple operations update related rows. Those characteristics make them the right match for an operational store that relies on named fields and row relationships for querying.

Plain text file is wrong because plain text lacks structured columns and rows and does not provide indexing or query capabilities that make retrieval efficient.

Spreadsheet is wrong because although it uses a rows and columns layout it is not a database engine designed for efficient, large scale querying or relational joins and it lacks the transactional and indexing features of a relational database.

Document database is wrong because document stores save semi structured documents such as JSON and they organize data around documents rather than fixed tables of named columns and rows. That model is optimized for flexible schemas and nested data rather than traditional relational queries.

When a question mentions named fields and rows and efficient querying think relational database and look for keywords like tables and joins.

The correct answer is Semi structured data.

Semi structured data refers to information that does not follow a rigid table like schema and where individual records can have different sets of attributes. It commonly appears as JSON or XML and supports nested or variable fields so it can represent flexible, evolving data models without a fixed set of columns.

Unstructured data is incorrect because unstructured data has little or no internal organization and includes things like plain text, images, audio, and video rather than records with named fields.

NoSQL document data is incorrect because that phrase describes a specific storage model or database type that often holds semi structured data but it is not the general descriptive term the question asks for.

Structured data is incorrect because structured data conforms to a strict tabular schema with a consistent set of fields across records and that is the opposite of the concept described.

When a question mentions a different set of attributes or a flexible schema pick the answer that describes data with variable fields rather than rigid tables.

The correct option is Apache Storm.

Apache Storm is a distributed, scalable, and fault tolerant stream processing system that was built for low latency real time processing. It can handle high volumes of streaming data while using comparatively modest compute resources because it is optimized for continuous event processing and parallelism. Apache Storm was designed with reliability in mind so that events are not lost and it supports replay and guaranteed processing semantics which matches the description in the question. It also interoperates with a variety of event sources such as Contoso Event Streams, Contoso IoT Hub, Apache Kafka, and RabbitMQ which aligns with the interoperability requirement.

Apache Flink is a modern stream processing engine that provides powerful features like event time semantics and strong state handling. It is not the intended answer here because the description in this question aligns with the historical design and characteristics of Storm rather than Flink.

Databricks is a managed analytics and data engineering platform built around Apache Spark and it focuses on batch and streaming analytics as a service. It is not a standalone lightweight stream processing engine in the way the question describes, so it is not the correct choice.

Apache Spark includes Structured Streaming and can process streams, but it is primarily a general purpose data processing engine and often uses a micro batch model by default which does not match the lightweight, low latency, event replay focused description as closely as Apache Storm.

When a question emphasizes a lightweight, low latency engine with built in replay and at least once semantics think of Apache Storm.

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

The correct answer is To accelerate query performance by building a structure for fast lookups.

Indexing creates auxiliary data structures that let the system locate records without scanning the entire dataset and that reduce query latency for targeted lookups and range queries. Building an index often involves tree or hash structures that optimize search operations and that trade increased storage use and slower writes for much faster read performance.

To create redundant copies of data for disaster recovery is incorrect because making redundant copies is achieved with replication, backups, and snapshots and not with an index.

To secure stored information by applying encryption at rest is incorrect because encryption protects data confidentiality and is implemented by storage encryption settings rather than by indexing.

To reduce the storage footprint by compressing data is incorrect because compression reduces disk usage while indexing usually increases storage overhead to support faster lookups.

When a question asks about indexing focus on whether it helps locate data quickly or whether it provides protection, redundancy, or storage reduction since indexing improves lookup speed and not those other functions.

The correct option is Scale the database to a higher DTU or vCore service tier to increase IOPS capacity.

This error indicates the Azure SQL Database is throttling work because it is hitting its resource limits for IOPS and concurrent work and scaling the database to a higher tier increases provisioned IOPS and overall capacity which reduces or eliminates the throttling.

When you apply scaling to a higher tier you get more CPU, memory, and I/O allowance and that directly addresses sustained high read and write rates that produce the 40501 “service is currently busy” response.

Add client side retry logic with exponential backoff and use connection pooling in the application is a good resiliency practice for transient errors but it does not solve a sustained capacity shortfall and so it will not prevent repeated 40501 throttling when the database is under provisioned.

Move the database to a dedicated single tenant instance or migrate it to SQL Server on a virtual machine would work but it is an unnecessary and more complex solution for this scenario since increasing the service tier is the simpler way to get more IOPS and throughput.

Open a support case to ask Microsoft to raise the IOPS limits for the current service tier is not appropriate because IOPS limits are determined by the chosen service tier and Microsoft will not raise limits for a single tenant within a tier instead you must change tiers to increase limits.

When you see a 40501 error check metrics for IOPS, DTU or vCore, and duration of saturation and treat scaling the service tier as the primary remediation while adding transient retries as a complement.

Big Data is the correct option.

Big Data is the field of technology and practice that focuses on extracting processing and analyzing datasets that are too large or too complex for conventional software tools to handle effectively.

Big Data solutions address challenges of high volume high velocity and wide variety of data by using distributed storage and processing frameworks and specialized analytics platforms so organizations can run large scale queries and machine learning workflows.

Unstructured data is incorrect because it describes a data format such as text images or audio and not the technology field that handles massive or complex processing tasks.

Semi-structured data is incorrect because it refers to data with some organizational properties like JSON or XML and not to the overall technology domain for scaling extraction processing and analysis.

Structured data is incorrect because it denotes highly organized data in fixed schemas such as relational tables and it does not name the technology field used to process very large or complex datasets.

When you see the phrase too large or complex for conventional software associate it with Big Data and think of the three Vs which are volume velocity and variety rather than the data format.

The correct answer is: Manual data sharding.

Manual data sharding splits a logical database into multiple shards and places those shards on different servers so transaction load can be distributed across machines. This is the common but difficult technique used when a single node can no longer handle very high transaction volumes and performance degrades.

Manual data sharding requires application changes for routing and for handling cross shard operations and rebalancing, and that additional complexity is why it is considered difficult even though it effectively spreads transactions across multiple servers.

Cloud SQL read replicas only scale read traffic because replicas are typically read only and they do not accept write transactions, so they do not spread write transactions across multiple servers.

Vertical scaling increases CPU memory or disk on a single server and that can help for a time but it does not distribute transactions across multiple servers and it hits hardware limits.

Cloud Spanner is a managed, horizontally scalable database that provides transparent sharding and can remove the need to shard manually, but it is a product rather than the manual technique the question asks for. Using Cloud Spanner is an alternative solution and not the named technique of manual data sharding.

Focus on whether the question asks for a technique or a managed service and notice whether reads or writes need scaling. Remember that read replicas help reads only and that sharding spreads writes horizontally but increases application complexity.

Relational database management systems that use SQL is correct because PostgreSQL, MariaDB, and MySQL are classic relational database engines that store data in tables and use SQL for defining and querying data.

These systems implement the relational model with schemas, tables, rows, and columns and they support ACID transactions, joins, and SQL DDL and DML statements which is why the relational SQL category fits them.

Cloud Spanner is incorrect because that name refers to Google Cloud’s managed, distributed database service and not to the open source engines PostgreSQL, MariaDB, or MySQL.

Non relational database management systems is incorrect because that category refers to NoSQL systems that do not primarily use SQL or fixed table schemas, and the three databases in the question are SQL based relational systems.

Hybrid database management systems is incorrect because that term typically describes multimodel or mixed architecture products and it does not accurately describe the primarily relational, SQL focused nature of PostgreSQL, MariaDB, and MySQL.

When you see specific database names check whether they use SQL and fixed table schemas. If they do they are very likely a relational database system.

The correct choice is Optimistic concurrency control.

Optimistic concurrency control is appropriate for a managed SQL instance that must serve many simultaneous users because it avoids long lived locks and blocking. Each transaction proceeds using a working snapshot and the system checks for conflicts at commit time and retries only the transactions that actually conflict, which improves throughput for high concurrency workloads.

Exclusive locks are not ideal because they force serialization of access to records and create contention that reduces scalability and increases latency in environments with many concurrent writers.

Snapshot isolation alone does not eliminate write conflicts in the same way as optimistic control and it can allow anomalies such as write skew. It is an isolation level rather than a full concurrency strategy that handles concurrent update retries.

Pessimistic concurrency control relies on locking to prevent conflicts and this causes blocking and poor throughput under heavy concurrent write loads. Pessimistic locking can be appropriate for very high conflict cases but it is usually not the best fit for a high throughput payments service where locks would become a bottleneck.

When questions mention many simultaneous users and high throughput look for answers that describe version checks, retries, or conflict detection instead of long lived locks.

The correct option is Azure Stream Analytics.

Azure Stream Analytics is a purpose built, real time analytics service that ingests and analyzes high volume event streams with low end to end latency. It supports SQL like streaming queries for on the spot computations and routing, and it integrates directly with ingestion sources such as Event Hubs and IoT Hub and with sinks like Power BI and Cosmos DB.

Azure Event Hubs is a high throughput ingestion and event streaming platform but it does not perform the real time analytics itself. It is commonly used to feed analytics services such as Azure Stream Analytics.

Azure Synapse Analytics is a unified analytics and data warehousing platform that focuses on large scale batch and interactive queries and big data processing. It can handle streaming scenarios with other components but it is not the specialized low latency stream analytics engine described in the question.

Azure Cosmos DB is a globally distributed NoSQL database that provides low latency storage and querying for structured data. It is not intended as a real time stream processing and analytics service for high throughput event streams.

When a question mentions both ingesting and analyzing real time event streams for low latency computations, distinguish between ingestion services and analytics engines. Pick the service that explicitly provides streaming query capabilities.

Model performance deterioration caused by shifts in the input data distribution is correct and this phrase describes the phenomenon known as data drift.

Data drift means the statistical properties of the inputs change between training and production and that shift can reduce a model’s accuracy over time. Teams need to monitor input distributions, compare them to training distributions, and respond with alerts, data fixes, or retraining so models remain reliable in production.

Changes in the relationship between features and target labels over time is incorrect because that describes concept drift and not data drift. Concept drift refers to the underlying mapping from inputs to labels changing and it is a related but distinct problem.

Vertex AI Model Monitoring is incorrect because it names a monitoring product rather than defining the phenomenon. The service can help detect data drift, but it is not the definition of data drift itself.

The reformatting and cleaning of raw inputs so they are suitable for training is incorrect because that describes data preprocessing and feature engineering. Those tasks prepare data for modeling and do not refer to changes in production data distributions over time.

When a question asks for a phenomenon pick the answer that defines a behavior and not a tool. Watch for words like distribution and performance to identify data drift.

The correct answers are Container and Database.

You can configure provisioned throughput at the Database level so that multiple containers share a pool of request units per second and you can manage throughput and costs centrally for related containers.

You can also configure provisioned throughput at the Container level to give a single container dedicated request units per second for predictable performance and isolation.

The option Partition is incorrect. Partitions are used to distribute data and throughput across physical storage and compute, but you do not directly provision throughput at a partition scope in Cosmos DB.

The option Item is incorrect. An item is an individual document and you cannot assign dedicated provisioned throughput to a single item.

When choosing answers remember that throughput is provisioned where you reserve RUs. Focus on the container and database scopes rather than individual items or internal partitions.

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

The correct option is 75.

Azure Synapse dedicated SQL pools can be scaled out to a maximum of 75 compute nodes to allow massively parallel processing for very large data workloads. You can pause the dedicated SQL pool to release compute resources and stop billing for compute while the pool is not needed. This capability was part of the service formerly known as Azure SQL Data Warehouse which is now provided under the Synapse Analytics branding.

30 is incorrect because the dedicated SQL pool supports far more parallel compute nodes than thirty and the documented maximum for this question is seventy five.

125 is incorrect because one hundred twenty five exceeds the supported maximum node count for the dedicated SQL pool in this context.

15 is incorrect because fifteen is well below the real maximum and would not match the documented scale out capability of the service.

180 is incorrect because one hundred eighty is larger than the supported maximum node count for a Synapse dedicated SQL pool in this scenario.

45 is incorrect because forty five is not the maximum and it underestimates the highest supported scale out level for the dedicated SQL pool.

When the exam asks about Synapse dedicated SQL pool limits focus on the maximum number of compute nodes and the pause feature. Remember that the pool can be paused to stop compute billing and that the maximum node count for this question is 75.

The correct answer is Data lakes hold raw unprocessed data while data warehouses hold structured curated datasets prepared for reporting.

Data lakes store data in its original form so teams can ingest a wide variety of formats and apply a schema when they read or process the data. Data warehouses store cleaned, modeled and optimized tables with a predefined schema so reporting and business intelligence queries run efficiently.

Data lakes are commonly used for flexible exploration and machine learning because they retain raw and semi structured or unstructured inputs. Data warehouses are commonly used for governed analytics and fast SQL reporting because they enforce schema and performance optimizations.

Google Cloud Storage and BigQuery is incorrect because it lists specific Google services rather than describing the fundamental conceptual difference between a lake and a warehouse. Both services can be part of an analytics architecture but they do not define the distinction.

Data lakes are optimized for real time analytics and data warehouses handle only batch workloads is incorrect because both architectures can support streaming and batch patterns depending on design and tooling. Modern warehouses can ingest streaming data and lakes can be tuned for low latency access for certain workloads.

Data lakes store only unstructured files while data warehouses accept only tabular structured data is incorrect because lakes can contain structured and semi structured data as well as unstructured files. Warehouses also support semi structured and nested types so the split is not strictly about file type or structure.

When you see questions about lakes versus warehouses focus on the distinction between raw versus curated data and the processing model such as schema on read versus schema on write. Choose the option that describes use case and data shape rather than specific products.

The correct option is Azure SQL server firewall rule.

A Azure SQL server firewall rule is a server level access control that allows or denies client public IP addresses for the SQL server. If the office public IP changed and the firewall rule was not updated the database will reject connections from the new IP while other resources that do not rely on that server level allowlist remain reachable.

Network Security Group controls traffic at the subnet or network interface level for virtual machines and subnets and it does not directly manage the public access rules of a platform service like Azure SQL server. Changes to an NSG would not explain why only the SQL database is unreachable.

Azure Private Endpoint configuration would move the SQL server to a private IP inside a virtual network and require access over that network. That configuration would prevent public internet access entirely and so it does not match the symptom of an office IP change breaking access while other internet reachable resources remain accessible.

Azure Role Based Access Control governs identity and management permissions and it does not control network level connectivity from the internet. RBAC would block operations for users without rights but it would not cause the network connection itself to be refused based on the client public IP.

When troubleshooting lost internet connectivity to a PaaS service check the service level firewall rules and client IP allowlists first and then examine network settings such as NSGs and private endpoints.

The correct answer is Setting automatic removal of items after a defined lifetime.

Setting automatic removal of items after a defined lifetime refers to the Time to Live capability in Azure Cosmos DB which automatically deletes items when their configured lifetime expires. Teams commonly use this feature to manage ephemeral data such as session state, caches, telemetry and logs. Enabling TTL at the container level provides a default lifetime and individual items can override that default with their own TTL value which helps reduce storage and indexing costs and simplifies data retention management.

Tuning queries and execution plans to lower latency is incorrect because TTL does not affect how queries are planned or executed. Query performance is managed through indexing policies and query design rather than automatic item expiry.

Defining consistency guarantees for replicated data is incorrect because consistency levels such as strong or eventual are configured separately and govern replication and read semantics rather than data lifetime.

Configuring indexing behavior for containers is incorrect because indexing policies control which properties are indexed and how indexing is performed and are distinct from TTL which only removes items after their time has elapsed.

Remember that TTL is about automatic deletion of data and not about query performance or consistency. Think of TTL for ephemeral workloads like sessions and telemetry to answer these questions quickly.

The correct answer is A flexible schema that allows frequent and easy restructuring of table rows.

Relational databases define a fixed schema made of tables and columns and they expect schema changes to be explicit operations such as ALTER TABLE. Because of that they are not designed for frequent and easy restructuring of table rows and that is why the bolded option is not a typical characteristic of a relational database.

Data integrity enforced through primary foreign and check constraints is a hallmark of relational systems. Primary keys foreign keys and check constraints are used to maintain data integrity so this option is typical of relational databases and therefore not the correct answer.

Cloud Spanner is a managed relational database offering that supports relational schema and SQL features. As a relational product it does not represent an atypical characteristic and so it is not the correct choice.

Atomicity consistency isolation and durability guarantees for transactions describe the ACID properties that relational databases commonly provide. Because transactional ACID guarantees are typical of relational systems this option is not the correct answer.

When you see choices that mention schema flexibility versus transactional guarantees or constraint language treat schema wording as the clue for non relational behaviour and treat ACID and constraint terms as clues for relational behaviour.

The correct option is ELT.

ELT is better suited for very large datasets because it loads raw data directly into scalable storage and relies on the data warehouse or data lake compute to perform transformations. This approach avoids a single transformation bottleneck and lets you scale storage and compute independently so ingestion can grow to meet high volume demands.

ELT also enables faster initial ingestion because raw data can be landed quickly and transformed later using parallel and elastic processing. Many cloud data platforms provide fast bulk loading and separate compute resources which align well with the ELT pattern for high throughput pipelines.

ETL is incorrect because it requires transforming data before loading which can create scaling and performance bottlenecks. Pre load transformation infrastructure must scale with every incoming dataset and that makes it harder to ingest very large volumes quickly.

Focus on where heavy compute takes place. If the question emphasizes large volumes and scalability then ELT is usually the preferred pattern because it leverages the target platform for scalable transforms.

The correct options are Partition key and Provisioned throughput.

Partition key is a container level setting that determines how items are distributed across physical partitions and it directly affects scalability and performance. A well chosen partition key spreads load and storage evenly and changing it requires a data migration, so it is a container scoped decision that impacts partitioning and throughput behavior.

Provisioned throughput refers to the RUs per second allocated to the container and it directly controls how many requests the container can sustain and at what latency. Adjusting provisioned throughput at the container level will change performance and cost and is the primary way to scale a container�s request capacity.

Read region is incorrect because read region placement and regional failover are configured at the account level and they influence replication topology and latency across regions rather than container level partitioning or the container�s throughput setting.

Consistency level is incorrect because consistency is an account level default that determines read guarantees across replicas and it affects correctness and latency tradeoffs rather than the container level partition key or the container�s provisioned throughput.

When a question asks about container level configuration think first about the partition key and the throughput. Account level settings like consistency or read regions do not count as container level options.

False is correct.

Microsoft does not guarantee 100 percent availability for Azure SQL Database. The service is covered by a published service level agreement that guarantees a high but not perfect uptime percentage and the SLA defines conditions for breaches and service credits rather than promising absolute availability.

You can improve resilience by using built in redundancy and failover features and by architecting for geo or zone redundancy. Those designs can raise practical availability but they do not change the fact that the vendor SLA specifies a percentage below 100 percent.

True is incorrect because it asserts that Microsoft guarantees complete availability. The official SLA for Azure SQL Database provides a specific uptime percentage and conditions for credit but it does not promise one hundred percent uptime.

When you answer cloud availability questions look for wording about the provider SLA and the guaranteed uptime percentage and do not assume an absolute guarantee.

The correct answer is Azure Time Series Insights.

Azure Time Series Insights is purpose built for interactive exploration and visualization of time series telemetry. It ingests streaming events from sources such as IoT Hub or Event Hubs and provides a low latency query engine and a browser based explorer that lets operators view live and historical trends and drill into anomalies.

Azure Time Series Insights also handles indexing and storage optimized for time stamped data so you can build a live dashboard without assembling a custom pipeline and visualization stack.

Azure Stream Analytics is a real time stream processing service that is excellent for filtering, aggregating, and routing event data. It does not provide a built in interactive time series explorer for live dashboards and it typically outputs results to another service for visualization.

Azure Data Lake Storage is a scalable store for large volumes of raw and processed data. It is designed for batch analytics and long term storage and it does not provide interactive, low latency dashboards for live telemetry.

Azure Synapse Analytics is focused on data warehousing and big data analytics. It is powerful for large scale queries and analytics jobs but it is not aimed at interactive, real time time series visualization for incoming telemetry.

When a question asks for a live, interactive telemetry dashboard look for services built specifically for time series exploration. Eliminate options that are primarily for storage or batch analytics.

Scheduled batch processing is the correct option.

Scheduled batch processing fits a dataset that arrives once every 24 hours because the workload can run on a fixed schedule to ingest and transform the entire file set in one job. The approach accepts higher end to end latency and is easy to orchestrate with tools that run periodic jobs and manage compute only when needed.

Cloud Pub/Sub is incorrect because it is a messaging and event ingestion service that is designed for asynchronous or streaming data delivery rather than for processing a single daily file transfer.

Continuous stream processing is incorrect because it targets constant event streams and low latency processing of individual events, and it is not the natural fit for a once per day batch file workload.

Real time analytics is incorrect because it emphasizes immediate, low latency insights and often sub second or second scale processing, which is unnecessary for a dataset that is delivered every 24 hours.

Look at the data arrival pattern and acceptable latency when you answer these questions. If data arrives on a schedule and latency can be minutes to hours then think batch processing. If data arrives continuously or needs sub second responses then think streaming.

A valid authorization token is the correct option when there are no network restrictions configured because the OrionDB account still requires authenticated API requests by default.

By default the service enforces request level authentication and a valid authorization token is presented in the Authorization header and validated on every API call. Tokens prove the caller identity and carry the permissions that determine whether the requested operation is allowed. This ensures that even if the network is open the API does not accept anonymous requests.

Client TLS certificate is incorrect because mutual TLS is an optional configuration and it must be explicitly enabled to require client certificates. It is not the default mechanism used to accept API requests when no network restrictions exist.

Username and password is incorrect because most modern APIs use token based authentication rather than sending raw username and password credentials with each API call. Basic credential schemes are not the default acceptance method for OrionDB accounts.

Google Cloud IAM is incorrect because IAM is an identity and access control system that can be integrated to issue or validate tokens and to grant permissions but the immediate requirement for API requests is possession of a valid authorization token. IAM itself is not the direct transport credential that is sent with each request unless it is used to obtain a token.

When network restrictions are not in effect focus on the request level controls and look for answers that mention authorization tokens or bearer credentials rather than network or basic credential options.

The correct answer is A federation feature that lets you execute T-SQL queries against external data sources.

PolyBase enables you to define external tables and query data that lives outside the Synapse SQL engine using standard T-SQL. It acts as a federation layer so you can join and query files in blob or data lake storage and remote relational systems as if they were local tables.

A method for compressing stored data to save space is incorrect because PolyBase is about querying and integrating external data and not about data compression. Compression is handled by storage formats and database compression features instead.

BigQuery is incorrect because that is a Google Cloud analytics service and not a capability of Synapse or PolyBase. The option names a different product rather than describing the PolyBase feature.

A real time ingestion pipeline for streaming IoT telemetry is incorrect because PolyBase does not provide streaming ingestion. Real time telemetry ingestion is handled by streaming services and pipelines such as Event Hubs and Stream Analytics or Synapse Pipelines and not by PolyBase.

When a question mentions querying external data with T-SQL or a federation layer think of PolyBase and external tables and rule out answers that describe compression, third party products, or streaming pipelines.

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

Azure HDInsight is the correct choice because it provides managed, production ready clusters for Hadoop, Spark, and HBase and it supports a lift and shift migration of on prem big data workloads.

Azure HDInsight runs native open source frameworks such as Hadoop, Spark, and HBase as managed clusters on Azure virtual machines so you can move existing jobs and configurations with minimal rework compared to rearchitecting for a different platform.

Azure Databricks is not the best lift and shift target for this scenario because it is a managed Spark platform optimized for analytics and machine learning and it does not provide a one to one managed HBase or full Hadoop cluster experience without additional changes.

Azure Synapse Analytics is an integrated analytics service and data warehouse that can run Spark workloads but it is not a direct replacement for on prem Hadoop and HBase clusters and would typically require reengineering of workloads.

Azure Data Lake Storage Gen2 is a scalable storage solution and it does not provide the compute frameworks or managed cluster services needed to run Hadoop, Spark, or HBase clusters on its own.

Azure Blob Storage is an object store and it also does not provide managed compute or a native HBase runtime so it cannot serve as a lift and shift destination for those clusters by itself.

When a question asks about moving existing Hadoop ecosystems look for services that offer managed versions of the same open source components so you can achieve a lift and shift migration with minimal code and configuration changes.

The correct answer is Virtually unlimited in the Hyperscale tier.

The Hyperscale tier is built to scale storage independently from compute and to add storage capacity by distributing data across nodes or shards, so it is described as virtually unlimited in the Hyperscale tier rather than having a small fixed maximum.

120 TB is incorrect because that number implies a fixed upper limit that applies to some non hyperscale configurations and it does not represent the advertised behavior of the Hyperscale tier.

3 TB is incorrect because that is a much smaller capacity that might apply to entry level offerings and it does not reflect the scale out design of Hyperscale.

1.2 PB is incorrect because it provides a specific petabyte cap, and Hyperscale is presented as offering practical unlimited growth rather than a fixed petabyte limit.

Read answer choices for phrases like virtually unlimited and for tier names such as Hyperscale. Those words usually indicate a scale out architecture and not a fixed storage cap.

Azure Table Storage is the correct answer.

Azure Table Storage is a NoSQL key value store that stores each record as a collection of properties and addresses records by a unique key, typically a partition key and a row key. It is part of Azure Storage and is designed for large amounts of structured but nonrelational data with fast lookup by key.

Azure SQL Database is a managed relational database that enforces schemas and uses SQL queries, so it does not match the schema free key value model described in the question.

Azure HDInsight Hadoop provides managed Hadoop clusters for big data processing and analytics and it is not a table based key value storage service for property collections.

Google Cloud Bigtable is a scalable NoSQL wide column store that uses a different data model optimized for very large analytical and time series workloads, so it is not the simple key value property collection service described here.

Look for phrases like collection of properties and unique key in the question because they usually point to a table or key value store and help you eliminate relational databases and big data cluster services.