DP-900 Practice Tests on Azure Data Fundamentals Exam Topics

A payment analytics team at Meridian Insights keeps account and transaction data in a document database and is evaluating trade offs compared to a classic SQL database. Which of the following is a disadvantage of selecting a non relational data store instead of a relational database?

• ❏ A. Relational databases are often a better choice for workloads that demand very high transactional throughput

• ❏ B. Non relational systems commonly do not enforce referential integrity automatically which means developers must manage related data consistency themselves

• ❏ C. Document and key value stores generally support flexible schemas which makes altering the data model simpler

• ❏ D. Some NoSQL solutions do not provide strong multirow transactional guarantees which can complicate atomic updates across multiple records

In an Azure environment which service matches this description It runs continuously and requires substantial configuration It is best suited for very compute intensive workloads where you need fine grained control The platform has a steep learning curve Much of its functionality is built on Apache projects Multiple cluster types are available and this service has been available for many years What service is being described?

• ❏ A. Azure Cosmos DB

• ❏ B. Azure Analysis Services

• ❏ C. Azure Synapse Analytics

• ❏ D. Azure HDInsight

• ❏ E. Azure Data Lake Storage

Which SQL statement inserts a single new record into a database table?

• ❏ A. SELECT

• ❏ B. BigQuery

• ❏ C. INSERT

• ❏ D. UPDATE

A fintech startup named Riverbank is evaluating Azure Database for PostgreSQL to host its transactional systems. Which statement accurately describes this service?

• ❏ A. It is a fully managed NoSQL database service

• ❏ B. It lacks compatibility with the PostgreSQL community edition

• ❏ C. It is a managed relational PostgreSQL offering that supports automated backups and point in time restore and maintains compatibility with the PostgreSQL community edition

• ❏ D. It is primarily intended for large scale analytical warehousing similar to BigQuery

Which Azure service lets you run serverless SQL queries directly over files stored in a data lake without provisioning or managing servers?

• ❏ A. Azure Data Factory

• ❏ B. Synapse Serverless SQL

• ❏ C. Azure Databricks

• ❏ D. Azure SQL Managed Instance

Examine the SQL statement shown below. DELETE FROM client_records What will this statement do?

• ❏ A. Remove the table structure and its data

• ❏ B. Delete every row from the client_records table

• ❏ C. Require embedding inside a SELECT statement to execute

• ❏ D. Fail because there is no target between DELETE and FROM

A cloud service for a regional retail chain must persist and query hierarchical structures such as management hierarchies and nested product families, and the team wants the most suitable Azure service for traversing those relationships efficiently, what should they choose?

• ❏ A. Azure Blob Storage

• ❏ B. Azure SQL Database

• ❏ C. Azure Cosmos DB with Table API

• ❏ D. Azure Cosmos DB with Gremlin API

Which Azure offering provides a fully managed cloud data warehouse that can consolidate information from multiple sources and enable advanced analytics and reporting?

• ❏ A. Azure Data Factory

• ❏ B. Azure Analysis Services

• ❏ C. Azure Synapse Analytics

• ❏ D. Azure Machine Learning

A cloud analytics team at BrightMart needs to restrict access in their data warehouse and they are comparing row level security with column level security. What is the primary difference between these two methods?

• ❏ A. Row level security is enforced only by the database engine while column level security must be applied in the client application

• ❏ B. Row level security limits which rows a user can access and column level security restricts access to individual columns within those rows

• ❏ C. Row level security is intended for structured tables while column level security is meant for unstructured data stores

• ❏ D. Row level security commonly uses predicates to filter records and column level security often uses masking or column policies to hide field values

Which term refers to large scale analytical workloads and which term refers to immediate response tasks and short window aggregations?

• ❏ A. Stream processing and batch processing

• ❏ B. Batch processing and stream processing

• ❏ C. Azure Synapse and Azure Stream Analytics

OrionSoft needs a place to keep a very large collection of small user avatar images and they want the most cost effective Azure storage option for this use case. Which Azure storage service should they choose?

• ❏ A. Azure Files

• ❏ B. Azure Cosmos DB

• ❏ C. Azure Blob Storage

• ❏ D. Azure Data Lake Storage

Which Azure offering is most appropriate for hosting a relational database that must support online transaction processing workloads for a multi tenant software as a service application?

• ❏ A. Azure Synapse Analytics

• ❏ B. Azure Cosmos DB

• ❏ C. Azure Database for MySQL

• ❏ D. Azure SQL Database

A retail analytics team needs a compressed binary file format that is efficient for column oriented processing and works well with both structured and semi structured datasets. Which file format should they choose?

• ❏ A. Avro

• ❏ B. CSV

• ❏ C. JSON

• ❏ D. Parquet

The analytics team at Summit Insights needs a single self service workspace where business analysts can prepare and explore datasets and it should integrate with reporting tools. Which Azure service is the best fit for this requirement?

• ❏ A. Azure Synapse Analytics

• ❏ B. Azure Data Factory

• ❏ C. Power BI Dataflows

• ❏ D. Azure Databricks

Which Azure service provides centralized monitoring and diagnostic capabilities for an Azure Cosmos DB account?

• ❏ A. Azure Application Insights

• ❏ B. Azure Monitor

• ❏ C. Azure Log Analytics

In the context of Azure services at Northwind Analytics which term fills the blank in this sentence [?] is a storage repository for very large volumes of raw unprocessed data because the data can be loaded and updated very quickly but it has not been organized into a format optimized for analytics?

• ❏ A. Azure Databricks

• ❏ B. Azure Blob Storage

• ❏ C. Azure Data Lake Storage

• ❏ D. Azure Synapse Analytics

A data engineering group at Northwind Systems is designing a large scale analytics pipeline that requires a distributed and scalable platform to run Apache Hadoop and Spark workloads. Which Azure service should they choose?

• ❏ A. Azure Databricks

• ❏ B. Azure Synapse Analytics

• ❏ C. Azure Machine Learning

• ❏ D. Azure HDInsight

A boutique retailer named HarborWave operates a LAMP application that uses MySQL Community edition on Linux and they want to move the app to Azure with as little application change as possible. Which Azure database service is the most straightforward choice for this migration?

• ❏ A. Azure SQL Managed Instance

• ❏ B. Azure Cosmos DB

• ❏ C. Azure Database for MySQL

• ❏ D. Azure Database for PostgreSQL

Atlas Retail operates an Azure Cosmos DB collection called client_profiles that stores customer records and metadata. You must ensure those records are encrypted when they reside on disk. Which Cosmos DB capability should you use?

• ❏ A. Client-side encryption

• ❏ B. Transparent Data Encryption (TDE)

• ❏ C. Automatic encryption at rest

• ❏ D. Customer-managed keys

Which cloud service model offers a managed underlying infrastructure with automatic scaling so you do not have to manage virtual machines?

• ❏ A. Infrastructure as a Service

• ❏ B. Platform as a Service

• ❏ C. Function as a Service

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

A buyer completed a subscription on the website and the application immediately inserted a new row into the orders_log table to capture that event. What type of database is designed to store this sort of record?

• ❏ A. Cloud Memorystore

• ❏ B. BigQuery

• ❏ C. Operational transactional database

• ❏ D. Data warehouse

A regional bookseller intends to move its database servers from an internal data center to a cloud platform managed service. What advantage does running the database on a PaaS provide compared with hosting it on site?

• ❏ A. Higher total cost of ownership

• ❏ B. Greater ability to scale resources automatically

• ❏ C. Larger day to day administration workload

• ❏ D. Removes the need to manage backups and updates entirely

When preparing datasets for analytics in an Azure cloud data pipeline for a retail insights team at NovaMetrics how do data cleansing and data transformation differ?

• ❏ A. Data cleansing is applied only to structured datasets while data transformation is used for unstructured data

• ❏ B. Data cleansing fixes errors and inconsistent values while data transformation converts and restructures data to meet the target schema or analytic needs

• ❏ C. BigQuery

• ❏ D. Data cleansing always occurs before ingestion and data transformation always takes place after ingestion

A data team at Meridian Grocers is evaluating storage options for a reporting platform and asks which two categories of data stores are not relational? (Choose 2)

• ❏ A. Contoso Database for MariaDB

• ❏ B. Graph database

• ❏ C. SQL database

• ❏ D. Document database

Which Azure service runs continuous SQL queries on IoT event streams and writes the aggregated results to Azure SQL Database?

• ❏ A. Azure Databricks

• ❏ B. Azure Stream Analytics

• ❏ C. Azure Functions

• ❏ D. Azure Cosmos DB

In Contoso Cloud Data Factory which orchestration construct moves data from the original source through a sequence of transformation steps to its final target?

• ❏ A. Dataflow

• ❏ B. Activities

• ❏ C. SQL statements

• ❏ D. Pipelines

Relational systems enforce rigid table schemas and they often require ongoing tuning to achieve good performance. Alternative NoSQL models store information as documents graphs key value pairs or column families. Azure Cosmos DB is a highly scalable cloud database for non relational workloads. Which Azure Cosmos DB API should you select when your data is organized as nodes and relationships in a graph?

• ❏ A. MongoDB API

• ❏ B. Cassandra API

• ❏ C. Table API

• ❏ D. Gremlin API

Which of the following data forms is considered unstructured when kept in cloud object storage?

• ❏ A. Relational database table

• ❏ B. JSON documents

• ❏ C. Files and objects in an object storage bucket

• ❏ D. NoSQL table entities

Do MySQL PostgreSQL and Contoso SQL service use precisely the same SQL syntax and vendor extensions?

• ❏ A. Most core query statements are portable but stored procedures and administrative commands differ

• ❏ B. No each engine implements a distinct SQL dialect and proprietary extensions

• ❏ C. Cloud SQL

• ❏ D. All systems strictly follow the ANSI SQL standard with no deviations

Which Azure service provides globally distributed, low latency, high throughput, key value storage for users worldwide?

• ❏ A. Azure Cache for Redis

• ❏ B. Azure Cosmos DB

• ❏ C. Azure Table Storage

A retail analytics team at Harborview Retail is reviewing customer demographics and transaction logs and they want to reveal trends in the count of purchases by age bracket. Which Microsoft Power BI visualization is most suitable for showing patterns in purchase counts across different age groups?

• ❏ A. Scatter plot

• ❏ B. Pie chart

• ❏ C. Stacked column chart

• ❏ D. Histogram

Which style of nonrelational data models entities and the connections between them and is best for datasets where you need to traverse from one entity to related entities such as a university department hierarchy or a professional networking platform?

• ❏ A. Document data

• ❏ B. Cloud Bigtable

• ❏ C. Relational SQL data

• ❏ D. Graph data

Determine whether each of the following statements is true. Managed platform database services in Contoso Cloud require less setup and configuration than deploying database systems on virtual machines. Managed platform database services in Contoso Cloud allow administrators to manage and update the underlying operating system. All managed platform database services in Contoso Cloud can be paused to reduce billing. Which of these statements are true?

• ❏ A. Yes, Yes, Yes

• ❏ B. Yes, No, No

• ❏ C. No, No, Yes

• ❏ D. No, Yes, No

A regional retailer called Harbor Supplies has the following SQL statement SELECT Clients.ClientName, Purchases.QuantityOrdered, Items.ItemName FROM Clients JOIN Purchases ON Clients.ClientID = Purchases.ClientID JOIN Items ON Purchases.ItemID = Items.ItemID What does this SQL statement return?

• ❏ A. Each client name appears only once together with the item name and quantity for each of their purchases

• ❏ B. BigQuery

• ❏ C. The statement returns each client name paired with the item name and the quantity for every purchase they have made

• ❏ D. The SQL fails because the table qualifiers make the syntax invalid

Which Azure service is best suited for storing property graphs and executing Gremlin traversals?

• ❏ A. Azure Databricks

• ❏ B. Azure Cosmos DB Gremlin API

• ❏ C. Azure SQL Database

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

An analytics team stores millions of JSON documents in an Azure Cosmos DB container and they must run advanced filtering and aggregation across the stored items. Which Cosmos DB API is most appropriate for performing these complex queries and aggregations?

• ❏ A. MongoDB API

• ❏ B. Table API

• ❏ C. SQL API

• ❏ D. Gremlin API

Within a cloud database platform used by Meridian Analytics what does the acronym “ACID” represent when describing transaction guarantees?

• ❏ A. Atomicity, Confidentiality, Integrity, Durability

• ❏ B. Availability, Consistency, Isolation, Durability

• ❏ C. Consistency, Availability, Partition tolerance

• ❏ D. Atomicity, Consistency, Isolation, Durability

Fill in the missing terms in this Azure statement. Azure SQL Database protects data with encryption. For data in transit it uses [A]. For data at rest it uses [B] encryption. For data in use it uses [C]?

• ❏ A. Google Cloud Key Management Service, Customer managed encryption keys, Homomorphic encryption

• ❏ B. NetNamedPipeBinding, HTTP Transport Security, Integrated Windows Authentication

• ❏ C. Transport Layer Security, Transparent Data Encryption, Always Encrypted

• ❏ D. Secure Sockets Layer, Service side encryption, Row level security

Which scenario best illustrates a streaming workload for continuous data processing?

• ❏ A. Transferring sales records that are older than 45 days to an offsite archive

• ❏ B. Streaming telemetry from distributed edge sensors in manufacturing plants

• ❏ C. Batch uploading point of sale transaction files once every 24 hours

• ❏ D. Exporting cloud operations metadata every 90 minutes for periodic analysis

How many replicas are maintained by Azure Zone Redundant Storage?

• ❏ A. One copy

• ❏ B. Three copies across three availability zones

• ❏ C. Six copies across regions

How does organizing data by rows compare to organizing it by columns in modern database systems?

• ❏ A. BigQuery stores data using a column oriented format

• ❏ B. Row oriented systems generally allow faster row inserts while column oriented systems speed up queries that read few columns

• ❏ C. Data is physically laid out as one record after another in row oriented storage and as groups of values per attribute in column oriented storage

• ❏ D. Row based storage is always more costly than column based storage

Fill in the missing term in this Microsoft Azure statement. [?] offers a fully managed Apache Kafka service on Azure. The [?] Cloud deployment of Kafka relieves users of deployment and operations tasks while delivering a pure managed service. Which name fits the blanks?

• ❏ A. Azure Event Hubs

• ❏ B. Streamsense Cloud

• ❏ C. Google Cloud Pub/Sub

• ❏ D. Kafka on HDInsight

Orion Cloud is a public cloud provider that hosts applications and infrastructure for many organizations and it provides services for both transactional and analytical data workloads. Which service is fully managed and enables a near lift and shift migration of an on premise SQL Server database without requiring schema changes?

• ❏ A. SQL Server on Orion Virtual Machines running Windows

• ❏ B. Orion SQL Managed Instance

• ❏ C. Orion SQL Database

• ❏ D. SQL Server on Orion Virtual Machines running Linux

You are to identify the missing word or words in the following sentence for a cloud platform scenario. Contoso Data Services fall into the [?] category and each data service handles configuration day to day management software updates and security for the databases it hosts?

• ❏ A. Infrastructure as a Service

• ❏ B. Database as a Service

• ❏ C. Software as a Service

• ❏ D. Platform as a Service

Which MongoDB comparison operator matches documents where a field’s value is greater than or equal to a specified threshold?

• ❏ A. $gt

• ❏ B. $gte

• ❏ C. $lte

• ❏ D. $in

Analysts at Northbridge Analytics use Power BI to produce highly formatted fixed layout files for printing and archiving such as PDF or Word. Which Power BI capability generates these paginated printable reports?

• ❏ A. BigQuery

• ❏ B. Dashboard

• ❏ C. Paginated report

• ❏ D. Interactive report

Which one of the following is not typically used as a file format for storing structured or tabular data?

• ❏ A. Comma separated values CSV

• ❏ B. Google Compute Engine

• ❏ C. JavaScript Object Notation JSON

• ❏ D. Extensible Markup Language XML

A retail analytics team running an Azure SQL Database sees increased write latency during peak sale events. Which scaling approach should be used to address the elevated write latency?

• ❏ A. Increase the database storage allocation

• ❏ B. Scale up compute resources by increasing vCores

• ❏ C. Provision read-only replicas for load distribution

• ❏ D. Increase storage throughput settings

Which feature in SQL Server and Azure Synapse Analytics lets you run T-SQL queries against external data stores and makes those external sources appear as tables in a SQL database?

• ❏ A. BigQuery

• ❏ B. Linked Server

• ❏ C. Azure Data Lake Storage Gen2

• ❏ D. PolyBase

In an analytics service, what term refers to a multi page collection of visualizations and related items?

• ❏ A. Dataset

• ❏ B. Tile

• ❏ C. Report

• ❏ D. Dashboard

A retail analytics team runs this statement SELECT ClientID , ClientName , ClientLocation FROM Clients What query language is this statement written in?

• ❏ A. JSON

• ❏ B. BigQuery

• ❏ C. SQL

• ❏ D. Python

Which of the following data types is not natively supported by Azure Cosmos DB?

• ❏ A. Number

• ❏ B. String

• ❏ C. Datetime

• ❏ D. Boolean

What principal benefit does Azure SQL Database Managed Instance provide when compared with an Azure SQL single database deployment?

• ❏ A. Cloud Spanner

• ❏ B. Near complete compatibility with on premises SQL Server

• ❏ C. Support for NoSQL APIs

• ❏ D. Lower operational cost

Within the context of the Fabrikam Cloud analytics service what word completes this sentence A(n) [?] is a method for displaying data such as a bar chart a color coded map or other graphical items that convey information and InsightStudio provides many different [?] types?

• ❏ A. Looker Studio

• ❏ B. Presentation

• ❏ C. Visualization

• ❏ D. BigQuery

Which SQL statement is used to modify existing row values in a table?

• ❏ A. SELECT

• ❏ B. UPDATE

• ❏ C. ALTER

Non relational systems commonly do not enforce referential integrity automatically which means developers must manage related data consistency themselves is the correct choice.

Non relational systems commonly do not enforce referential integrity automatically which means developers must manage related data consistency themselves captures the key disadvantage that many document and key value stores do not provide built in foreign key constraints or automatic referential checks. That means the application must implement logic to prevent orphaned records and to keep related documents consistent which increases development and testing effort and raises the risk of subtle bugs.

Relational databases are often a better choice for workloads that demand very high transactional throughput is incorrect because it states a generalization about throughput. Throughput depends on the specific database design and scaling approach and many non relational systems are optimized for very high write and read throughput at large scale, so this statement is not a reliable disadvantage of non relational stores.

Document and key value stores generally support flexible schemas which makes altering the data model simpler is incorrect because it describes an advantage rather than a disadvantage. Flexible schemas are a common benefit of document and key value stores since they avoid costly schema migrations and allow records to evolve independently.

Some NoSQL solutions do not provide strong multirow transactional guarantees which can complicate atomic updates across multiple records is misleading as the best answer here. Historically that limitation existed in some NoSQL systems, but many modern document databases and distributed databases now provide multi document transactions or other consistency models, and the question focuses on the lack of automatic referential integrity which is a more fundamental disadvantage for relational features.

When an exam question asks for a disadvantage check whether each option describes an advantage or an architectural limitation. Focus on what the database enforces automatically versus what the application must manage.

The correct answer is Azure HDInsight.

Azure HDInsight fits the description because it is a long lived cluster service that typically runs continuously and requires significant configuration and administration. It is built on open source Apache projects such as Hadoop, Spark, Kafka, and HBase so it exposes fine grained control over cluster settings and resource allocation. The service is well suited for very compute intensive workloads where you need direct control over the cluster and tuning, and it has been available in Azure for many years.

Azure Cosmos DB is incorrect because it is a globally distributed NoSQL database service and not a cluster based Apache ecosystem for running compute intensive jobs. It focuses on low latency data access and multi model data storage rather than managing long running compute clusters.

Azure Analysis Services is incorrect because it provides managed analytical modeling and tabular semantic models for BI workloads and not a general purpose Apache cluster platform. It is aimed at modeling and query performance for reporting rather than raw compute clusters.

Azure Synapse Analytics is incorrect in this context because it is a unified analytics service that includes serverless and provisioned SQL pools and managed Spark pools and it is a newer integrated platform. The description points to a more traditional, Apache based cluster service with long lived clusters and heavy configuration so HDInsight is the better match.

Azure Data Lake Storage is incorrect because it is a storage service for big data files and not a compute cluster product. It provides scalable storage for analytics but it does not itself run the Apache compute frameworks that the question describes.

Watch for clue words like Apache, long running, and fine grained control. Those clues usually point to a managed Hadoop or Spark cluster offering rather than a database, storage, or fully integrated analytics platform.

The correct option is INSERT.

The INSERT statement adds a single new record to a database table. You specify the target table and either a VALUES clause or a subquery so that one new row is created with the provided column values.

In SQL you commonly write INSERT INTO table_name (column1, column2) VALUES (value1, value2) to add one record. The important point is that INSERT creates new data rather than reading or modifying existing data.

SELECT is wrong because it is used to retrieve rows from a table and it does not create new records. It returns data and does not perform insertion.

BigQuery is wrong because it is the name of a Google Cloud data warehouse service and not a SQL statement. You can run SQL inside BigQuery but the service name itself does not insert records.

UPDATE is wrong because it modifies existing rows in a table and does not add a new row. It is used to change values after a record already exists.

When you read options think about whether the verb implies creating, reading, or modifying data and then match that action to INSERT, SELECT, or UPDATE.

The correct answer is It is a managed relational PostgreSQL offering that supports automated backups and point in time restore and maintains compatibility with the PostgreSQL community edition.

It is a managed relational PostgreSQL offering that supports automated backups and point in time restore and maintains compatibility with the PostgreSQL community edition describes Azure Database for PostgreSQL because the service runs the PostgreSQL community engine and provides managed capabilities such as automated backups, point in time restore, automated patching, and scaling along with built in high availability and security features.

It is a fully managed NoSQL database service is incorrect because the service is relational and implements PostgreSQL SQL semantics rather than NoSQL data models.

It lacks compatibility with the PostgreSQL community edition is incorrect because Azure Database for PostgreSQL is based on and maintains compatibility with the PostgreSQL community edition so applications and tools written for PostgreSQL work with the managed service.

It is primarily intended for large scale analytical warehousing similar to BigQuery is incorrect because Azure Database for PostgreSQL is optimized for transactional and relational workloads and not for the large scale distributed analytics use cases that a serverless data warehouse targets.

When a question mentions automated backups and point in time restore look for services described as managed and community compatible to identify a relational managed database offering rather than a NoSQL or analytics warehousing product.

The correct answer is Synapse Serverless SQL.

Synapse Serverless SQL lets you run T SQL queries directly over files stored in Azure Data Lake Storage without provisioning or managing database servers. It provides on demand compute and you pay per query so there is no need to run or maintain a dedicated SQL server for ad hoc exploration of lake data.

Synapse Serverless SQL supports querying Parquet CSV and JSON through OPENROWSET or external tables and it is integrated in the Synapse workspace to enable serverless ad hoc analytics over data lake files.

Azure Data Factory is focused on orchestration and ETL and it moves and transforms data rather than offering an interactive serverless SQL engine to query files in a data lake.

Azure Databricks provides Spark based analytics and normally runs on clusters that you manage or autoscale so it is not the dedicated serverless T SQL query service for querying files in a data lake.

Azure SQL Managed Instance is a managed instance of SQL Server for hosting databases and it is not designed to run serverless queries directly over data lake files.

When a question mentions querying files in a data lake without managing servers think Synapse Serverless SQL because it runs T SQL on files on demand and bills per query rather than requiring provisioned compute.

The correct answer is Delete every row from the client_records table.

The statement Delete every row from the client_records table corresponds to the SQL command DELETE FROM client_records which has no WHERE clause so it removes every row and leaves the table structure in place. The DELETE command affects rows and not the table definition.

Remove the table structure and its data is incorrect because dropping the table definition and all of its data is done with a DROP TABLE statement, not with DELETE.

Require embedding inside a SELECT statement to execute is incorrect because DELETE is an independent data manipulation statement. You may use subqueries in a WHERE clause but you do not embed DELETE inside a SELECT to run it.

Fail because there is no target between DELETE and FROM is incorrect because the correct and common syntax is DELETE FROM table_name and no additional target is required between DELETE and FROM.

When you see DELETE FROM without a WHERE clause remember that it will remove all rows but it will keep the table schema.

The correct answer is Azure Cosmos DB with Gremlin API.

Azure Cosmos DB with Gremlin API is a graph database service and it is designed specifically for representing and traversing relationships such as management hierarchies and nested product families. Graph databases store entities as nodes and relationships as edges which makes traversals across many hops efficient and natural.

Azure Cosmos DB with Gremlin API supports the Gremlin traversal language which is optimized for walking complex relationship patterns and Cosmos DB adds low latency indexing and global scale so queries across large, connected datasets perform well in production.

Azure Blob Storage is object storage for unstructured data and it does not provide native graph query or traversal capabilities so it is not suitable for relationship traversals.

Azure SQL Database can represent hierarchies with recursive queries or the hierarchyid type but relational recursive queries tend to be less efficient and harder to scale for deep or highly connected graph traversals compared with a purpose built graph database.

Azure Cosmos DB with Table API implements a key value or wide column model for scalable lookup and telemetry workloads and it does not support Gremlin graph traversals, so it is not appropriate for relationship heavy traversal needs.

When a question focuses on traversing many to many relationships or deep hierarchies think graph databases and the Gremlin traversal model as the most suitable choice.

The correct answer is Azure Synapse Analytics.

Azure Synapse Analytics is a fully managed cloud data warehouse and analytics platform that can ingest and consolidate data from multiple sources and enable advanced analytics and reporting. It provides dedicated and serverless SQL pools for data warehousing, integrates with data ingestion and orchestration services, and connects to reporting tools for business intelligence.

Azure Data Factory is incorrect because it is a data integration and orchestration service used to move and transform data and not a data warehouse that stores and queries consolidated data for analytics.

Azure Analysis Services is incorrect because it provides semantic modeling and analytical processing for BI workloads and does not serve as the underlying fully managed cloud data warehouse for large scale storage and query processing.

Azure Machine Learning is incorrect because it focuses on building training and deployment pipelines for machine learning models and it is not a service for consolidating multiple data sources into a managed warehouse for reporting.

Look for words like fully managed and data warehouse in the question to distinguish services that store and query consolidated data from services that only move data or build models.

Row level security limits which rows a user can access and column level security restricts access to individual columns within those rows is correct.

Row level security works by limiting which records a user can see or query. It is implemented with policies or predicates that filter rows at query time or at the storage layer so users only retrieve the subset of data they are authorized to view.

Column level security controls access to specific fields inside those rows. It is used to hide or restrict sensitive attributes so a user may see a record but not the protected columns within that record. This is enforced with column access controls, views, or masking features depending on the database.

Row level security is enforced only by the database engine while column level security must be applied in the client application is wrong because both row and column restrictions can be enforced by the database engine or by layered controls such as views and middleware. Client side enforcement is not a requirement for column level security.

Row level security is intended for structured tables while column level security is meant for unstructured data stores is wrong because both concepts apply to structured data models where rows and columns exist. Column level controls do not target unstructured stores, and unstructured systems use different access patterns.

Row level security commonly uses predicates to filter records and column level security often uses masking or column policies to hide field values is misleading and therefore not the best choice. Row filtering often uses predicates but that is an implementation detail rather than the defining difference. Masking is one technique for protecting columns but column level security more broadly includes access grants, policy tags, and view-based controls. The primary difference remains whether access is limited by rows or by columns.

Focus on what is being restricted when you compare security methods. If access is limited by which records a user can see think row level security. If access is limited by which fields inside records a user can see think column level security.

The correct answer is Batch processing and stream processing.

Batch processing refers to large scale analytical workloads that process very large volumes of data in grouped runs and perform comprehensive aggregations over longer time windows. Stream processing refers to immediate response tasks that handle data continuously and compute short window aggregations to provide low latency results.

Stream processing and batch processing is incorrect because it reverses the mapping and would suggest that stream processing is the large scale analytical approach and that batch is used for immediate responses which is not the standard meaning.

Azure Synapse and Azure Stream Analytics is incorrect because those are specific vendor services rather than the general terms that describe processing approaches. The question asks for the workload terms and not for particular products.

Look for keywords such as large scale or immediate and map them to batch or stream. Also think about latency and window size to decide which processing model fits.

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

Azure Blob Storage is the correct choice for storing a very large collection of small user avatar images because it is cost effective object storage that scales massively and offers tiering to optimize costs based on access patterns.

Azure Blob Storage is designed for unstructured object data and charges primarily for stored bytes and operations which makes it cheaper than database or file services when storing many images. You can use hot, cool, and archive tiers and lifecycle management to lower costs for objects with different access patterns and you can combine it with a CDN for efficient global reads.

Azure Files provides managed SMB and NFS file shares and is intended for scenarios that need file system semantics and lift and shift migrations. It is not the most cost effective option for simple avatar image storage because it typically costs more and adds features that are not required for object storage.

Azure Cosmos DB is a globally distributed NoSQL database that charges for storage and request units which makes it significantly more expensive for storing large numbers of binary blobs. It is optimized for low latency queries and transactional data rather than bulk object storage.

Azure Data Lake Storage Gen2 builds on blob storage and adds a hierarchical namespace for analytics workloads. It is aimed at big data and filesystem semantics and is not the most cost effective choice for many small avatar images unless you specifically need analytics or hierarchical file operations.

Focus on whether the workload needs object storage, file system semantics, or database features. For many small static files such as avatars choose object storage and consider tiering and lifecycle rules to minimize cost.

Azure SQL Database is the most appropriate offering for hosting a relational database that must support online transaction processing workloads for a multi tenant software as a service application.

Azure SQL Database is a fully managed relational database service that is designed for OLTP workloads and it provides built in high availability, automated backups, and strong transactional consistency. It offers scaling options that fit multi tenant SaaS, including elastic pools to share resources across many tenant databases and Hyperscale to support very large transactional databases.

Azure SQL Database also includes enterprise features such as row level security, dynamic data masking, and advanced threat protection, and it integrates with SQL Server tooling which is often required for complex transactional SaaS applications.

Azure Synapse Analytics is focused on large scale analytics and data warehousing rather than low latency transactional processing, so it is not the right choice for OLTP driven multi tenant SaaS workloads.

Azure Cosmos DB is a globally distributed NoSQL service optimized for massive scale and multi model document or key value workloads, and it does not provide the same relational schema, SQL Server compatibility, or OLTP semantics expected by many transactional SaaS applications.

Azure Database for MySQL is a managed relational option and it can run OLTP workloads, but the question context points to the Microsoft SQL Server compatible managed service with features like elastic pools and tight SQL tooling integration, which makes Azure SQL Database the preferred answer for typical multi tenant SaaS scenarios targeting the SQL Server ecosystem.

When a question mentions relational OLTP and multi tenant SaaS focus on managed relational services that offer elasticity and SQL compatibility. Watch for keywords like elastic pools or Hyperscale as clues that the answer is Azure SQL Database.

The correct option is Parquet.

Parquet is a compressed binary, column oriented file format that is designed for efficient analytic queries where you read only some columns. Parquet stores data by column and it supports nested data types and a schema which makes it suitable for both structured and semi structured datasets. Parquet also enables predicate pushdown and column pruning which reduce IO and improve query performance in BigQuery, Spark, and other analytics engines.

Avro is a compact binary serialization format that is row oriented rather than column oriented. Avro is excellent for streaming and record serialization but it does not provide the same IO savings for column based analytics as a columnar format.

CSV is a plain text format that lacks a typed schema and nested structure. CSV is not a compressed binary format and it is inefficient for column oriented processing at scale.

JSON is a text based semi structured format that is flexible for nested data. JSON is verbose and not columnar which makes it less efficient for large scale analytic workloads.

When the question asks for a compressed binary format that is efficient for column oriented processing choose Parquet. Remember that Avro is row oriented and that text formats like CSV and JSON are not optimal for columnar analytics.

The correct answer is Power BI Dataflows.

Power BI Dataflows provides a single self service workspace where business analysts can prepare and explore datasets using Power Query based transformations and store reusable entities that integrate directly with Power BI reporting and other analytics tools. The service is designed for business user self service data preparation and it supports connections to many data sources as well as storage in Azure Data Lake for downstream use.

Azure Synapse Analytics is a powerful integrated analytics platform for large scale data warehousing and big data processing, but it is more infrastructure and developer oriented than a lightweight self service workspace for business analysts who need a simple prepare and explore experience.

Azure Data Factory is an orchestration and ETL service that is ideal for building data pipelines and integrating data at scale, but it does not provide the interactive, self service workspace and native reporting integration that Power BI Dataflows offers for business analysts.

Azure Databricks is focused on collaborative data engineering and data science with notebooks and Spark processing, and it is not primarily intended as a self service BI workspace for business analysts to prepare datasets and hook them directly into reporting tools.

Focus on the requirement words such as self service workspace and integrate with reporting tools. Those phrases usually point to Power BI Dataflows or Power BI features rather than pipeline or big data compute services.

The correct answer is Azure Monitor.

Azure Monitor is the centralized service that collects metrics and diagnostic logs from Azure resources including Azure Cosmos DB and it provides built in alerting, visualizations, and workbooks to investigate performance and operational issues.

Azure Monitor can route Cosmos DB diagnostic settings to different targets and it uses the Azure Log Analytics workspace as the queryable store for logs when you need to run deep queries and analysis.

Azure Application Insights is focused on application performance monitoring and distributed tracing. It is useful for instrumenting an application that accesses Cosmos DB but it is not the central monitoring service for Cosmos DB.

Azure Log Analytics is the log storage and query engine that works with Azure Monitor. It is not the monitoring service itself so it is not the correct choice for the centralized monitoring service.

When asked which service centralizes monitoring across Azure resources think Azure Monitor. Remember that Azure Log Analytics is the storage and query component and that Application Insights is for application telemetry.

The correct answer is Azure Data Lake Storage.

Azure Data Lake Storage is designed to store very large volumes of raw and unprocessed data and it supports fast ingestion and frequent updates without requiring the data to be organized first. The service provides features and performance optimizations that make it suitable as a landing zone for raw data so downstream analytics processes can transform and organize the data when needed.

Azure Databricks is a managed Apache Spark analytics platform and it is focused on processing and analyzing data rather than serving as a primary storage repository for raw unprocessed data.

Azure Blob Storage is general purpose object storage for unstructured data and while it can hold large amounts of data it does not by itself provide the hierarchical namespace and certain big data features that distinguish a data lake designed for analytics at scale.

Azure Synapse Analytics is an integrated analytics service that combines data warehousing and big data processing and it is intended for querying and processing data rather than acting as the raw storage repository described in the question.

When a question mentions raw or unprocessed data that must be ingested quickly think of a data lake and not a data warehouse. Look for wording about organization and optimization for analytics to distinguish the two.

The correct answer is Azure HDInsight.

Azure HDInsight is a fully managed cloud service that provisions and runs distributed clusters for Hadoop and Spark and it supports other Hadoop ecosystem components such as HBase and Kafka. It is designed for large scale analytics pipelines and provides scalability, integration with Azure storage, and enterprise features like security and monitoring which make it suitable for running Hadoop and Spark workloads at scale.

Azure Databricks is a managed, optimized platform for Apache Spark that excels at interactive analytics and machine learning with collaborative notebooks and optimized runtimes, but the question calls for a distributed platform that explicitly runs the broader Hadoop ecosystem as well as Spark which makes HDInsight the better fit.

Azure Synapse Analytics provides an integrated analytics service with data warehousing and big data capabilities and it can run Spark pools, but it is focused on unified analytics and SQL data warehousing rather than being a dedicated managed Hadoop cluster service.

Azure Machine Learning is targeted at building, training, and deploying machine learning models and it is not a managed service for provisioning Hadoop clusters or for running general Hadoop ecosystem workloads.

When a question explicitly asks for a managed platform for Hadoop and Spark look for the service that lists Hadoop ecosystem support and cluster management. Remember that Databricks focuses on Spark while HDInsight covers the broader Hadoop ecosystem.

The correct answer is Azure Database for MySQL.

Azure Database for MySQL is a managed MySQL service that is compatible with MySQL Community edition so the LAMP application can be migrated with minimal code and schema changes. It supports the MySQL wire protocol and SQL dialect and it handles backups, patching, scaling and high availability so operational tasks are reduced while the application behavior remains familiar.

Azure Database for MySQL typically requires only a change to the connection string and maybe minor configuration tuning during migration which makes it the most straightforward choice for moving a MySQL on Linux application to Azure.

Azure SQL Managed Instance uses the Microsoft SQL Server engine and T SQL dialect so migrating from MySQL would require schema and query conversions and significant application changes.

Azure Cosmos DB is a multi model NoSQL service and it is not MySQL compatible so moving a MySQL relational app to Cosmos DB would require rewriting the data model and application queries.

Azure Database for PostgreSQL provides a PostgreSQL engine and is not compatible with MySQL so migrating would require data type and SQL changes and therefore more application modifications than using a MySQL compatible managed service.

When a question asks about minimizing application changes for a MySQL based app pick a managed service that is MySQL compatible so you usually only need to update the connection string and not rewrite queries or the schema.

The correct option is Automatic encryption at rest.

Automatic encryption at rest is the Cosmos DB capability that ensures data is encrypted while it is persisted to disk. Cosmos DB encrypts stored data by default using service managed keys and this automatic server side encryption meets the requirement to have records encrypted when they reside on disk.

Client-side encryption is incorrect because it refers to encrypting data before it leaves the client. That approach protects sensitive values under client control but it is not the built in server side disk encryption feature that the question asks about.

Transparent Data Encryption (TDE) is incorrect because TDE is an Azure SQL database feature and it is not the mechanism used by Azure Cosmos DB for encryption at rest.

Customer-managed keys is incorrect in this context because it describes who controls the encryption keys rather than the automatic server side encryption capability itself. Customer managed keys can be used to control key ownership and rotation but the option that directly ensures disk encryption is automatic encryption at rest.

When a question asks about data being encrypted on disk look for options that mention server-side or automatic encryption at rest. Client-side encryption and key management are related concepts but they answer different security concerns.

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The correct option is Platform as a Service.

This model provides a managed application platform where the cloud provider handles servers, networking, operating systems, and runtime patching and it also offers automatic scaling so you do not manage virtual machines. That lets developers focus on writing and deploying application code while the provider manages the underlying infrastructure.

Infrastructure as a Service is incorrect because IaaS gives you virtual machines and low level resources and you are responsible for managing the operating system and instance scaling unless you add additional managed services. It therefore does not match the premise of not managing VMs.

Function as a Service is incorrect because FaaS provides a serverless, function level execution model that automatically scales but it is a narrower execution environment for discrete functions. The question asks for a managed platform for running applications without managing VMs which describes PaaS rather than the more limited FaaS model.

When a question says you do not manage virtual machines look for wording about a managed application platform or automatic scaling and think Platform as a Service rather than answers that imply managing VMs.

The correct option is Operational transactional database.

An operational transactional database is designed for Online Transaction Processing and it is optimized for many small, fast writes and immediate consistency. It provides ACID transactions and row level operations which make it the appropriate place to insert an order event as soon as a buyer completes a subscription.

Managed services on Google Cloud such as Cloud SQL or Cloud Spanner are examples of operational transactional databases you would use to capture order rows and other application events in real time.

Cloud Memorystore is an in memory key value store and cache. It is useful for low latency lookups and session data but it is not intended to be the durable primary store for transactional order records.

BigQuery is an analytical, columnar service built for large scale queries and analytics workloads. It is optimized for scanning and aggregating large datasets rather than for fast, small transactional inserts and immediate consistency.

Data warehouse refers to systems built for reporting and analytics and they are typically used for batch or streamed analytics rather than as the primary transactional store for individual order events.

When a question describes immediate single row inserts and the need for consistency think transactional systems. If the scenario focuses on analytics or large scans then choose an analytic store instead.

Greater ability to scale resources automatically is the correct answer.

A platform managed database gives a bookseller a Greater ability to scale resources automatically because the cloud provider manages the underlying infrastructure and offers built in autoscaling features. This means storage can grow without manual reconfiguration and some managed databases can adjust capacity to match load so the application stays responsive during traffic spikes.

For example Google Cloud managed databases include automatic storage increases for Cloud SQL and horizontal scaling capabilities in Cloud Spanner which let an organization expand capacity with less manual intervention and less operational friction.

Higher total cost of ownership is incorrect because moving to a managed service usually reduces operational overhead and staffing costs even if the listed service fees seem higher. Total cost of ownership often falls when you factor in reduced maintenance time and fewer infrastructure management tasks.

Larger day to day administration workload is incorrect because a PaaS reduces routine administration by handling provisioning, patching, and many operational tasks. The bookseller would generally have a smaller day to day operational burden compared with hosting databases on site.

Removes the need to manage backups and updates entirely is incorrect because managed services do not eliminate responsibility for backups and updates. Providers perform automated backups and offer update mechanisms, but the customer still must configure retention, verify restores, choose maintenance windows, and test upgrades and recovery procedures.

When a question mentions a managed database think about who handles operational tasks and whether the benefit is about reduced management overhead or about features like automatic scaling.

Data cleansing fixes errors and inconsistent values while data transformation converts and restructures data to meet the target schema or analytic needs is the correct option.

Data cleansing is focused on improving data quality by removing duplicates and correcting typos and inconsistent values and by handling missing or invalid entries so analyses are based on accurate inputs.

Data transformation reshapes and converts data so it fits the target schema and the analytic requirements and this includes tasks such as type conversion aggregation joining and pivoting to produce the structures that downstream tools expect.

Data cleansing is applied only to structured datasets while data transformation is used for unstructured data is incorrect because both cleansing and transformation can apply to structured and unstructured data and the distinction is about purpose rather than data format.

BigQuery is incorrect because it is not an explanation of the difference between cleansing and transformation and it is also a cloud data warehouse product that is unrelated to the conceptual distinction asked for in this question.

Data cleansing always occurs before ingestion and data transformation always takes place after ingestion is incorrect because cleansing and transformation can occur at multiple stages of a pipeline and the timing depends on architecture and requirements rather than a fixed order.

When answering compare the purpose of each process rather than where it runs. Data cleansing is about fixing quality and data transformation is about reshaping for the target schema or analysis.

The correct options are Graph database and Document database.

Graph database is non relational because it models data as nodes and relationships and it does not rely on fixed relational tables or normalized schemas. Graph stores are optimized for traversing relationships and they use graph oriented queries instead of the table joins typical of relational systems.

Document database is non relational because it stores semi structured documents such as JSON or BSON and it allows flexible, schema less records that do not map to normalized relational tables. Document stores support rich queries over documents but they are considered NoSQL rather than relational databases.

Contoso Database for MariaDB is incorrect because MariaDB is a relational database management system and solutions based on MariaDB use SQL tables and transactions. The product name indicates a relational engine rather than a NoSQL store.

SQL database is incorrect because SQL refers to relational database systems that use structured schemas and tables. The question asks which categories are not relational and SQL databases belong to the relational category.

Focus on the data model rather than product names when classifying stores and remember that document and graph databases are NoSQL and not relational.

Azure Stream Analytics is the correct option.

Azure Stream Analytics is a managed real time stream processing service that runs continuous SQL like queries over inputs such as IoT Hub and Event Hubs and it can write aggregated results directly to sinks including Azure SQL Database.

Azure Databricks is a Spark based analytics platform that can process streaming data but it relies on Spark and notebooks rather than a built in continuous SQL engine tailored to IoT event streams, so it is not the service described.

Azure Functions provides serverless event driven compute for custom processing and integrations but it does not offer a native continuous SQL query engine and you would need to implement ongoing aggregation logic yourself, so it is not the right match.

Azure Cosmos DB is a globally distributed NoSQL database for storing application data and it does not run continuous SQL queries over event streams as a streaming analytics engine, so it is not the correct choice.

When you see the phrase continuous SQL queries together with IoT event streams think of a stream processing service that supports a SQL like language and built in outputs to databases such as Azure Stream Analytics.

The correct answer is Pipelines.

Pipelines are the orchestration construct that group and sequence work. A pipeline defines a workflow that runs a set of activities in order and moves data from the original source through transformation steps to the final target.

Dataflow is incorrect because data flows are the transformation components that perform row and column level transforms and they run as activities inside a pipeline rather than providing the overall orchestration.

Activities is incorrect because activities are the individual tasks such as copy or execute that perform work. Activities are contained within a pipeline which is the object that sequences and orchestrates them.

SQL statements is incorrect because SQL statements are commands executed against a database and they can be used by an activity, but they do not provide orchestration across multiple steps or connectors.

When a question asks about orchestration look for the term pipeline or workflow since it groups and sequences activities that perform the actual work.

The correct option is Gremlin API.

The Gremlin API is built for graph data and it represents data as vertices and edges which map directly to nodes and relationships. It implements the Apache TinkerPop Gremlin traversal language so you can efficiently query and traverse graphs in Azure Cosmos DB and it is the natural choice when your model is a graph.

MongoDB API targets document databases and stores JSON like documents, so it is not optimized for representing or traversing node and relationship graphs.

Cassandra API implements a wide column store compatible with Apache Cassandra and it is designed for high throughput column oriented workloads rather than graph traversals and relationships.

Table API is intended for key value and table storage scenarios similar to Azure Table storage and it does not provide native graph traversal capabilities, so it is not suitable for graph data.

None of the listed APIs are deprecated and they remain available, but each API targets a distinct data model which is why the graph focused Gremlin API is the correct selection for node and relationship data.

When a question describes data as nodes and relationships pick the API designed for graphs such as Gremlin rather than document or key value APIs.

The correct answer is Files and objects in an object storage bucket.

Files and objects in an object storage bucket are stored as binary blobs without a fixed schema and they commonly include images videos audio logs backups and arbitrary files which makes them unstructured when kept in object storage.

Relational database table represents structured data with fixed schemas columns and typed fields so it is not considered unstructured.

JSON documents are typically considered semi structured because they use keys and nested structures that can be parsed and queried and they differ from unstructured binary objects.

NoSQL table entities are stored with defined keys and attributes in database services and they are considered structured or semi structured rather than unstructured.

When you see object storage on an exam think of files and blobs as unstructured data and contrast that with databases which hold structured or semi structured records.

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The correct answer is No each engine implements a distinct SQL dialect and proprietary extensions.

Relational database systems implement the ANSI SQL core but each vendor adds its own dialect and proprietary extensions. These differences show up in data types, built in functions, procedural languages, system catalogs, index and storage options, and administrative commands so SQL written for one engine often needs changes to run on another.

Most core query statements are portable but stored procedures and administrative commands differ is not the best choice because it understates the scope of differences. The distinction is broader than stored procedures and administration and includes types, functions, query planner hints, and other vendor specific features.

Cloud SQL is incorrect because it names a managed database service rather than answering whether MySQL PostgreSQL and Contoso SQL share identical syntax. It does not address SQL dialect compatibility.

All systems strictly follow the ANSI SQL standard with no deviations is incorrect because in practice vendors implement extensions and do not adhere to the standard in every detail. The ANSI standard defines a core but most engines extend or diverge from that core to add features and optimizations.

When a question asks about SQL compatibility look for mentions of vendor extensions or procedural languages because those are strong clues that the dialects will differ from one engine to another.

The correct answer is Azure Cosmos DB.

Azure Cosmos DB is a globally distributed, multi model database service that offers turnkey multi region replication and single digit millisecond latencies at the 99th percentile. It supports key value access patterns and provides provisioned throughput and strong SLAs for latency and availability, which makes it suitable for worldwide users who need low latency and high throughput.

Azure Cache for Redis is an in memory cache that provides very low latency and high throughput for cached data, but it is primarily an application cache rather than a globally distributed durable key value store with built in multi region replication and the same global SLAs as Cosmos DB.

Azure Table Storage is a simple NoSQL key value store that works well for single region scenarios and for cost effective storage, but it does not provide the turnkey global distribution, multi region replication, and latency SLAs that Azure Cosmos DB provides. Table Storage is part of Azure Storage and is not the recommended choice when you need worldwide low latency access.

When a question asks about worldwide low latency and high throughput look for services that advertise global distribution and multi region replication in their documentation.

Stacked column chart is the correct choice for showing purchase counts by age bracket.

Stacked column chart places discrete age groups along the horizontal axis and purchase counts on the vertical axis which makes it easy to compare totals across brackets. It also supports stacking so you can break each bar into subcategories such as product type or channel to reveal additional patterns within each age group.

Scatter plot is not suitable because it is intended to show relationships between two continuous variables and it does not summarize counts by category effectively.

Pie chart is not ideal because it emphasizes proportions of a whole and becomes difficult to read when there are many age brackets or when you need to compare counts and show subgroup breakdowns.

Histogram displays the distribution of a continuous variable using bins and is useful for exploring raw age distribution. It is less appropriate when you already have defined age brackets or when you need clear comparisons and stacked subgroup views.

When a question asks about counts across named groups prefer column or bar visuals. Use histogram for raw continuous distributions and choose a stacked column chart when you need to compare categories and show subgroup breakdowns.

The correct answer is Graph data.

Graph data models entities as nodes and relationships as edges so the connections are first class and traversals across related entities are fast and natural. Graph databases are built for queries that follow relationships across many hops, which makes them ideal for scenarios like a university department hierarchy or a professional networking platform where you need to traverse from one entity to related entities.

Document data stores hierarchical or JSON like documents and it is excellent for flexible schemas and retrieving whole objects, but it is not optimized for efficient multi hop traversals across many connected entities.

Cloud Bigtable is a wide column store that excels at very large scale single row or range lookups and low latency reads and writes, but it does not provide native graph traversal capabilities and it is not the best choice for relationship heavy queries.

Relational SQL data represents relationships with foreign keys and joins and it is suitable for many structured and transactional workloads, but many hop traversals that require repeated joins become complex and can perform worse than using a purpose built graph model.

When a question emphasizes traverse or many connected hops pick a graph model. Eliminate document stores and wide column stores when the primary need is relationship traversal rather than document retrieval or scalable key based access.

Yes, No, No is correct. The first statement is true and the second and third statements are false.

The first statement is true because managed platform database services require less setup and configuration than deploying database systems on virtual machines. The cloud provider handles the operating system, patching, backups, high availability and many maintenance tasks, so administrators do not need to install and configure the OS and basic database platform in the same way they would on a VM.

The second statement is false because managed platform database services normally do not allow administrators to manage and update the underlying operating system. Providers keep control of the OS to ensure security and stability and they perform regular patching and maintenance. Some managed offerings give more surface area for configuration, but full OS access is not a general property of managed databases.

The third statement is false because not all managed platform database services can be paused to reduce billing. Certain serverless or pause-capable tiers can suspend compute while preserving storage, but many managed databases do not support pausing and storage or other resources may continue to incur charges.

Yes, Yes, Yes is incorrect because the second and third parts are not generally true for managed services. Providers usually manage the OS and not all managed databases support pausing.

No, No, Yes is incorrect because the first part is actually true and the third part is not generally true. Managed services usually reduce setup compared with VMs and not every service can be paused.

No, Yes, No is incorrect because the first part is true and the second part is false. Managed platform databases typically reduce setup effort and they do not normally allow administrators to manage the underlying operating system.

When evaluating statements about managed services, focus on who is responsible for the operating system and check whether a pause or stop feature is explicitly documented rather than assuming it applies to all managed databases.

The correct option is The statement returns each client name paired with the item name and the quantity for every purchase they have made.

This SELECT lists Clients.ClientName, Purchases.QuantityOrdered, and Items.ItemName and it joins Clients to Purchases and Purchases to Items, so each resulting row represents a single purchase and shows the client name, the item name, and the quantity ordered.

Because the joins are inner joins the result only includes purchases that have matching client and item records and you will therefore see one row per purchase rather than one row per client.

Each client name appears only once together with the item name and quantity for each of their purchases is incorrect because the query does not aggregate or group results and it returns a separate row for each purchase so a client with multiple purchases appears on multiple rows.

BigQuery is incorrect because it is not an answer that describes the query result and it does not explain what the SELECT statement returns.

The SQL fails because the table qualifiers make the syntax invalid is incorrect because qualifying columns with their table names is valid SQL and the JOIN syntax shown is standard and syntactically correct in SQL engines that support qualified names.

When you see queries with JOIN focus on whether the statement produces one row per related record or whether it aggregates. Check the SELECT list and whether there is a GROUP BY to decide if values will repeat.

Azure Cosmos DB Gremlin API is the correct answer because it is the Azure service designed to store property graphs and execute Gremlin traversals.

The Azure Cosmos DB Gremlin API implements the Apache TinkerPop Gremlin traversal language and provides a native property graph model where you can store vertices and edges with arbitrary properties. It is a fully managed, globally distributed database with automatic indexing and low latency reads which makes it suitable for both transactional and analytical graph traversals using Gremlin.

Azure Databricks is a Spark based analytics platform that can run graph processing libraries like GraphX or GraphFrames but it does not offer a native Gremlin API or a managed property graph database. It is intended for large scale analytics and machine learning rather than directly serving Gremlin traversal workloads in a transactional graph store.

Azure SQL Database is a relational database that provides T SQL graph extensions for node and edge modeling but it does not support Gremlin traversals and it is not a native property graph database. It can model graphs for some use cases but it is not the recommended option when the question specifically asks for Gremlin API support.

When a question mentions Gremlin look for services that explicitly support Gremlin or Apache TinkerPop and prefer native graph databases instead of analytics or relational platforms for transactional graph workloads.

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The correct option is SQL API.

SQL API is the native document query model for Azure Cosmos DB and it supports a rich SQL like language for querying JSON documents with advanced filtering, aggregation functions such as COUNT, SUM, AVG and GROUP BY, JOINs across items, user defined functions and stored procedures. It also leverages Cosmos DB indexing and execution optimizations so it is most appropriate for running complex queries and aggregations across millions of documents.

MongoDB API is a compatibility layer that exposes the MongoDB wire protocol and it is useful for migrating MongoDB applications. It can perform aggregations with the MongoDB aggregation pipeline but it is not the native Cosmos DB query model and so it is less appropriate when the question asks for the most suitable API for advanced, native Cosmos DB querying and aggregations.

Table API is designed for key value and simple table storage scenarios and it does not provide the rich SQL like querying and aggregation features needed for complex analytical queries over JSON documents.

Gremlin API targets graph data and traversal queries and it is optimized for nodes and edges rather than document level aggregations, so it is not appropriate for running advanced filtering and aggregations across JSON documents.

When a question asks about complex queries and aggregations over JSON documents choose the API that is native to Cosmos DB for document queries and think SQL API first.

The correct answer is Atomicity, Consistency, Isolation, Durability. This is the standard meaning of ACID when describing transaction guarantees in a database platform.

Atomicity ensures that a transaction either completes entirely or has no effect. Consistency ensures that a transaction moves the database from one valid state to another. Isolation ensures that concurrent transactions do not interfere with each other. Durability ensures that once a transaction commits its changes are permanent and survive system failures.

Atomicity, Confidentiality, Integrity, Durability is incorrect because confidentiality and integrity are security properties and not part of the ACID transaction model.

Availability, Consistency, Isolation, Durability is incorrect because availability is a system level concern and is not one of the ACID transaction guarantees.

Consistency, Availability, Partition tolerance is incorrect because those three terms describe the CAP theorem for distributed systems and do not represent the ACID properties for transactions.

When you see ACID think transactional guarantees such as Atomicity, Consistency, Isolation, and Durability. Keep the CAP theorem separate since it covers Consistency, Availability, and Partition tolerance.

The correct option is Transport Layer Security, Transparent Data Encryption, Always Encrypted.

Transport Layer Security is used to protect data in transit by encrypting the network connection between client and server so that eavesdroppers cannot read transmitted data. Azure SQL requires encrypted client connections and uses Transport Layer Security as the modern protocol for that purpose.

Transparent Data Encryption protects data at rest by encrypting the database files and backups on disk so that stored data and backups remain unreadable without the proper keys. Azure SQL implements the at rest protection through Transparent Data Encryption.

Always Encrypted protects data in use by keeping plaintext values only on the client side and encrypting sensitive columns so the database engine never sees the cleartext. This design helps ensure confidentiality during query processing and prevents certain server side exposures.

Google Cloud Key Management Service, Customer managed encryption keys, Homomorphic encryption is incorrect because it mixes a Google Cloud product with a research technique and a general key management phrase that do not match the specific Azure SQL features for transit, at rest, and in use. The Azure answers are Transport Layer Security, Transparent Data Encryption, and Always Encrypted and not Google Cloud services or homomorphic encryption.

NetNamedPipeBinding, HTTP Transport Security, Integrated Windows Authentication is incorrect because those terms come from Windows communication frameworks and authentication methods rather than the encryption technologies used by Azure SQL. Azure SQL uses Transport Layer Security for network encryption and not NetNamedPipeBinding or HTTP Transport Security.

Secure Sockets Layer, Service side encryption, Row level security is incorrect because Secure Sockets Layer is an older protocol that has been superseded by modern TLS and is therefore deprecated. The phrase Service side encryption is vague and does not name the Azure mechanism like Transparent Data Encryption. The term Row level security is an access control feature and it does not encrypt data in use.

When a question separates data in transit, at rest, and in use map them mentally to TLS, TDE, and Always Encrypted and then eliminate options that reference other clouds or unrelated features.

The correct answer is Streaming telemetry from distributed edge sensors in manufacturing plants.

Streaming telemetry from distributed edge sensors in manufacturing plants represents a continuous flow of small, frequent events that must be processed as they arrive for real time monitoring, alerting, and anomaly detection. Streaming systems are designed for low latency and continual ingestion, which matches the characteristics of telemetry from distributed sensors.

Transferring sales records that are older than 45 days to an offsite archive is an archival activity that is infrequent and tolerant of latency, so it is a batch workload rather than streaming.

Batch uploading point of sale transaction files once every 24 hours explicitly describes periodic bulk uploads and therefore fits batch processing, not continuous streaming.

Exporting cloud operations metadata every 90 minutes for periodic analysis is a scheduled, periodic export and is closer to a scheduled batch or micro batch workflow rather than a true continuous streaming workload.

When deciding between streaming and batch ask whether data arrives continuously and requires near real time processing. If it does then the workload is likely streaming.

The correct answer is Three copies across three availability zones.

This option describes Azure Zone Redundant Storage which maintains three replicas of your data across three distinct availability zones within the same region to provide resiliency against zone level failures and to preserve availability and durability.

Distributing replicas across separate zones keeps data accessible even if a single zone goes down and this is the key distinction of zone redundant storage compared with geo redundant approaches that copy data to another region.

One copy is incorrect because Azure storage does not rely on a single copy for production workloads. Azure uses multiple replicas so that hardware faults or infrastructure failures do not result in data loss or prolonged downtime.

Six copies across regions is incorrect for zone redundant storage. That description better matches geo redundant configurations which keep three copies in a primary region and three in a secondary region for a total of six replicas and it is not how ZRS operates.

When a question asks about Azure storage redundancy pay attention to the words zone and region and match them to the expected replica counts while remembering that ZRS implies three copies across zones.

The correct answer is Data is physically laid out as one record after another in row oriented storage and as groups of values per attribute in column oriented storage.

This is correct because in a row oriented storage layout each complete record is written sequentially so reading or writing whole rows is efficient for transaction style workloads. In contrast a column oriented storage layout stores each attribute together so scanning a few columns across many rows is much faster and compression is often better.

The two approaches have trade offs and are chosen based on workload. A row oriented storage design favors fast retrieval of full rows and simple inserts. A column oriented storage design favors analytics that read many rows but only a subset of columns and it can reduce I O through better compression.

BigQuery stores data using a column oriented format is not the best choice for this question because it states a product fact instead of describing the general difference between row and column organization. The question asks about the comparison of storage layouts and not about a single service.

Row oriented systems generally allow faster row inserts while column oriented systems speed up queries that read few columns is too broad to be marked correct here. Modern systems include hybrid and optimized ingestion paths so insert and query performance depends on implementation and workload and is not universally captured by that single generalization.

Row based storage is always more costly than column based storage is wrong because the word always makes it an absolute claim. Cost and efficiency depend on data types, compression, access patterns, and the specific database implementation.

When answering these questions look for options that describe the physical layout of data. Be wary of absolute words like always and of choices that name a single product instead of stating a general property.

The correct answer is Streamsense Cloud.

Streamsense Cloud provides a fully managed Apache Kafka service on Azure and the Cloud deployment removes the need for users to provision, configure, and operate Kafka clusters so teams can focus on building applications rather than managing infrastructure.

Azure Event Hubs is not correct because it is a native Azure event streaming service that offers a Kafka protocol endpoint for compatibility but it is not described as a pure managed Apache Kafka cloud provider in the way the question specifies.

Google Cloud Pub/Sub is not correct because it is a Google Cloud messaging service and not an Azure offering and it implements different messaging semantics than Apache Kafka.

Kafka on HDInsight is not correct because it delivers Kafka on HDInsight clusters which still require cluster provisioning and operational management and it is not the fully hands off managed Cloud Kafka service the question describes.

Read the question for the cloud vendor and the exact phrasing such as fully managed versus managed cluster to quickly eliminate options that are compatible or similar but not the described offering.

The correct answer is Orion SQL Managed Instance.

Orion SQL Managed Instance is a fully managed, instance compatible service that is designed to enable near lift and shift migrations from on premises SQL Server with minimal or no schema changes. It provides high compatibility with SQL Server features and supports instance level capabilities such as SQL Agent, cross database queries, and native backup and restore which makes migrating databases much easier while removing the need to manage the underlying OS and patching.

SQL Server on Orion Virtual Machines running Windows is not correct because it is an infrastructure service. You must manage the virtual machine operating system, patching, backups, and high availability yourself which does not meet the definition of a fully managed near lift and shift service.

Orion SQL Database is not correct because it targets single databases or elastic pools and it does not offer full instance level compatibility. That means you may need to change schemas or application logic when moving databases that depend on instance features or cross database functionality.

SQL Server on Orion Virtual Machines running Linux is not correct for the same reason as the Windows VM option. It is IaaS and requires you to manage the OS and database infrastructure which is not a fully managed, near lift and shift migration path.

When a question mentions a near lift and shift or minimal schema changes look for services that advertise instance compatibility or near 100 percent compatibility with SQL Server. Managed instances are often the right choice rather than VMs or single database services.

The correct answer is Platform as a Service.

Platform as a Service refers to a managed platform where the provider assumes responsibility for the runtime, middleware and platform operations and it often includes managed services such as databases. In the scenario each Contoso Data Service handles configuration, day to day management, software updates and security for the databases it hosts which aligns with the platform responsibilities a PaaS provider performs.

Platform as a Service lets customers focus on their applications and data while the provider manages the underlying platform and shared services like backups, patching and security. That is why a collection of managed data services is categorized as PaaS rather than a lower level or end user service model.

Infrastructure as a Service is incorrect because IaaS supplies virtualized compute, storage and networking while customers retain responsibility for operating systems, middleware and database management. The scenario describes provider managed updates and security which goes beyond IaaS.

Database as a Service is incorrect in this question because DBaaS normally names specific managed database offerings. The prompt points to a broader platform of data services that manage multiple platform level responsibilities, so the intended category is PaaS rather than a narrowly focused DBaaS label.

Software as a Service is incorrect because SaaS delivers complete end user applications that are consumed directly by users and it does not describe a platform that hosts and manages database services for customers to build and run their own applications on.

When you see the provider managing runtimes, updates and security think platform level management and not just individual products. That clue usually points to PaaS.

The correct answer is $gte.

$gte matches documents where a field value is greater than or equal to the specified threshold. You use this comparison operator when you want to include the threshold value as well as larger values in your query results.

$gt is incorrect because it matches only values that are strictly greater than the threshold and it does not include the threshold value itself.

$lte is incorrect because it matches values that are less than or equal to the threshold rather than greater than or equal to it.

$in is incorrect because it matches any value from a list of specified values rather than testing for greater than or equal to a single threshold.

When an exam question asks about inclusive comparisons remember that $gte includes the threshold while $gt does not. Read the operator symbols carefully to determine inclusion or exclusion.

The correct answer is Paginated report.

Paginated report describes Power BI reports that are designed for fixed layouts and precise pagination so they can be printed or exported to PDF and Word with consistent, pixel perfect formatting. These reports are built with paginated reporting technology and Power BI Report Builder so they support headers, footers, page breaks, and tabular layouts across many pages.

BigQuery is incorrect because it is a Google Cloud data warehouse and not a Power BI report type or a feature that generates printable reports. It stores and queries data rather than producing formatted print outputs.

Dashboard is incorrect because dashboards are single page, interactive summaries made of tiles and live visuals and they are intended for monitoring and exploration rather than fixed page printing. Dashboards do not provide the pixel perfect pagination needed for archived PDFs or Word documents.

Interactive report is incorrect because interactive Power BI reports are optimized for on screen exploration and dynamic interaction and they do not guarantee a fixed, printable page layout like paginated reports do.

When a question mentions fixed layout, pixel perfect, or printable pages think of paginated reports as the Power BI feature for exporting to PDF or Word.

The correct answer is Google Compute Engine.

Google Compute Engine is an infrastructure service that provides virtual machines and related cloud resources and it is not a file format used to store structured or tabular data.

Comma separated values CSV is a plain text file format that stores rows and columns and it is commonly used for tabular data and spreadsheets, so it is not the correct choice.

JavaScript Object Notation JSON is a text based data interchange format that represents structured and semi structured data with objects and arrays and it is frequently used to store structured records, so it is not the correct choice.

Extensible Markup Language XML is a markup language used to represent hierarchical structured data and it is commonly used for structured documents and data interchange, so it is not the correct choice.

When deciding which option is not a file format look for entries that name a service or a product rather than a data format and focus on the role each item plays.

Scale up compute resources by increasing vCores is the correct option.

Scale up compute resources by increasing vCores reduces write latency because adding vCores increases CPU and memory and it usually raises the database service limits for log throughput and IOPS in the vCore purchasing model. Increasing compute addresses transactional and log flush bottlenecks that cause elevated write latency during peak sale events and it is the standard way to improve write performance for an Azure SQL Database.

Increase the database storage allocation is incorrect because adding storage capacity gives more space but does not necessarily improve write performance. Storage allocation increases disk size but Azure SQL Database IOPS and log throughput are tied to service tier and compute, so simply growing storage will not resolve compute or log related write latency.

Provision read-only replicas for load distribution is incorrect because read replicas spread read workload and do not accept write traffic. Read scaling helps read latency and throughput but it will not reduce write latency on the primary database during heavy transactional peaks.

Increase storage throughput settings is incorrect for a single Azure SQL Database because there is no independent per-database storage throughput knob in most service tiers. Storage performance is typically governed by the compute tier, service tier, or specific architectures like Hyperscale, so the practical way to lower write latency is to scale compute rather than attempting to adjust a separate storage throughput setting.

When a question mentions higher write latency under peak load think first about compute and transaction log throughput. Check metrics like CPU, log bytes/sec, and log write waits to decide whether to scale vCores.

PolyBase is correct.

PolyBase lets you run T SQL queries against external data stores and it makes those external sources appear as tables by creating external tables and external data sources in the SQL catalog. It is supported in both SQL Server and Azure Synapse Analytics and it can query files in Hadoop clusters or Azure Blob Storage or Azure Data Lake and it can also access other relational sources through connectors.

Linked Server is incorrect. Linked Server is a SQL Server feature for querying OLE DB data sources from within SQL Server but it is not the cross platform external table engine used by Azure Synapse Analytics and it is not the feature the question describes.

BigQuery is incorrect. BigQuery is a Google Cloud data warehouse service and it is not a feature of SQL Server or Azure Synapse Analytics.

Azure Data Lake Storage Gen2 is incorrect. This is a storage service where data can reside and PolyBase can read from it but it is not itself the SQL feature that exposes external tables.

When a question asks about querying external data from within SQL Server or Synapse look for PolyBase as the built in external table engine and rule out options that are storage services or cloud specific warehouses.

The correct option is Report. A Report in analytics typically refers to a multi page collection of visualizations and related items that users can navigate to explore different aspects of the data.

A Report groups pages so you can present multiple views and narratives in a single deliverable. Reports support page level filters linked visuals and explanatory text which makes them ideal for detailed multi page analysis and storytelling with data.

Dataset is incorrect because a dataset contains the underlying data and schema that visualizations use and it does not itself represent a multi page presentation of visuals. The dataset is about the source data rather than arranged pages of content.

Tile is incorrect because a tile normally denotes a single visualization or widget placed on a page or canvas and it does not represent a collection of pages. Tiles are individual components rather than multi page groupings.

Dashboard is incorrect because dashboards are generally a single page or canvas that presents a curated set of visuals for at a glance monitoring. Dashboards focus on a one page summary while reports are designed for multi page detailed exploration.

When a question mentions a multi page collection think report and contrast it with single page concepts like dashboard or individual components like tile, and remember that dataset refers to the underlying data rather than the presentation.

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

The correct answer is SQL.

The statement uses the SELECT and FROM keywords which are characteristic of the SQL query language for relational databases. It retrieves specific columns from a table named Clients and that structure matches standard SQL query syntax.

JSON is a data interchange format for representing structured data and it is not a query language. The example is not written in JSON.

BigQuery is a Google Cloud data warehouse service that runs queries but it is not the query language itself. BigQuery accepts queries written in SQL so the example is SQL syntax rather than a BigQuery language.

Python is a general purpose programming language and the shown statement is not valid Python syntax. Python would require different syntax and would not use raw SQL keywords in this way without a database interface.

When you see keywords like SELECT and FROM look for SQL or a SQL dialect as the correct answer on the exam.

Datetime is the correct answer because Azure Cosmos DB does not provide a native DateTime type in its JSON document model.

Azure Cosmos DB stores data as JSON documents and supports native JSON types such as numbers strings booleans arrays objects and null. Because of this there is no dedicated Datetime value type and dates are typically stored as ISO 8601 strings or as epoch numbers depending on the application and query needs.

Number is incorrect because Cosmos DB supports numeric values as JSON numbers and handles integers and floating point values natively.

String is incorrect because string values are a native JSON type in Cosmos DB and are commonly used to store textual data and date strings when needed.

Boolean is incorrect because boolean values are part of the JSON data model and are supported directly by Cosmos DB.

Remember that Cosmos DB stores data as JSON documents so think in terms of native JSON types like number, string, and boolean rather than looking for a special datetime type.

Near complete compatibility with on premises SQL Server is the correct option.

Azure SQL Database Managed Instance provides near native compatibility with the SQL Server engine so you can migrate existing on premises SQL Server instances with minimal application changes. The managed instance supports many instance scoped features such as SQL Server Agent, cross database queries, native backup and restore patterns, and other capabilities that make lift and shift migrations much easier compared with a single database deployment.

Cloud Spanner is incorrect because it is a Google Cloud product and not a benefit of an Azure service. Cloud Spanner is a different vendor offering that focuses on global scale and horizontal relational scaling.

Support for NoSQL APIs is incorrect because Managed Instance targets SQL Server relational workloads and does not provide NoSQL APIs. Azure offers NoSQL capabilities through services such as Cosmos DB which are designed for document and key value patterns.

Lower operational cost is incorrect because cost depends on sizing and features and is not the principal distinction between managed instance and a single database. Managed Instance reduces administrative effort and improves compatibility but it is chosen primarily for feature parity and easier migrations rather than guaranteed lower price.

When the exam compares managed instance and single database focus on feature parity and lift and shift migration benefits rather than cost or services from other cloud vendors.

The correct answer is Visualization.

A Visualization is the term used for a method of displaying data such as a bar chart a color coded map or other graphical items that convey information and insight and InsightStudio provides many different Visualization types to represent trends comparisons and distributions visually.

Looker Studio is a reporting and dashboard product for building reports and interactive dashboards and it is not the generic term for a method of displaying data.

Presentation typically means a slide deck or a structured set of slides for presenting content and it does not specifically refer to a chart or graphical display used to visualize data.

BigQuery is a cloud data warehouse for storing and analyzing large datasets and it is a storage and query service rather than a type of visualization.

Focus on the wording that asks for a method for displaying data and pick the option that names a visual representation such as visualization rather than a product or a platform.

The correct answer is UPDATE.

A UPDATE statement changes existing row values in a table by specifying new values for one or more columns and by using an optional WHERE clause to limit which rows are affected. The typical form is UPDATE table_name SET column = expression WHERE condition, and if you omit the WHERE clause the statement updates every row in the table.

The SELECT statement is for querying and returning data and it does not modify stored row values. SELECT is read only and therefore it cannot be the correct choice for changing existing rows.

The ALTER statement is used to change the database schema such as adding or dropping columns or modifying column types. ALTER affects the structure of a table and not the values in its rows, so it is not the right answer.

Keep the distinction between DML and DDL clear in your mind. DML statements like UPDATE change data while DDL statements like ALTER change structure.