DP-900 Practice Exam 54 — 20 Questions

Question 1

A software company is migrating an on-premises SQL Server database to Azure SQL Database. The database currently uses SQL Server Agent jobs for regular maintenance tasks. The company wants to minimize code changes during migration. Which Azure SQL Database feature should they use to replace SQL Server Agent jobs?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Elastic Jobs. Elastic Jobs is the Azure SQL Database feature specifically designed to replace SQL Server Agent jobs by allowing you to run T-SQL scripts across multiple databases on a schedule. It provides a job scheduler and execution engine that is compatible with existing T-SQL maintenance scripts, minimizing code changes during migration from on-premises SQL Server.

Answer

Azure Functions

Answer

Azure Automation

Answer

SQL Server Agent (available in Azure SQL Database)

Question 2

A retail company ingests daily sales data from multiple stores as CSV files stored in Azure Blob Storage. The data must be cleaned and transformed using Spark, then loaded into Azure Synapse Analytics for large-scale reporting. The pipeline must run on a schedule, handle failures with retries, and minimize manual intervention. Which combination of Azure services should they use to orchestrate and execute this pipeline?

Accepted Answer

Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.. Azure Data Factory provides the orchestration and scheduling layer, Azure Databricks executes the Spark-based cleaning and transformation, and Azure Synapse Analytics serves as the target data warehouse for large-scale reporting. This combination supports retry policies for failure handling and minimizes manual intervention through automated pipeline execution.

Answer

Azure Stream Analytics, Azure Data Lake Storage, and Power BI.

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Azure Functions, Azure SQL Database, and Azure Analysis Services.

Answer

Azure Logic Apps, Azure HDInsight, and Azure Cosmos DB.

Question 3

Which TWO of the following are valid use cases for Azure Queue Storage?

Accepted Answer

Building a serverless workflow with Azure Functions. Option A is correct because Azure Queue Storage is commonly used to trigger serverless workflows: Azure Functions has a Queue Storage trigger/binding that polls the queue and invokes the function when messages arrive, making it a valid use case for building serverless workflows. Option D is correct because Azure Queue Storage provides asynchronous, durable message passing that decouples front-end and back-end components, allowing the web tier to enqueue work while back-end workers process it independently. Option B is not correct because storing JSON documents for querying is a document-database scenario, best served by Azure Cosmos DB or Azure Table Storage, not a queue. Option C is not correct because storing large binary objects for a website is blob storage, i.e., Azure Blob Storage, not Queue Storage. Option E is not correct because real-time event streaming for analytics is handled by Azure Event Hubs (or Kafka), whereas Queue Storage is designed for simple asynchronous messaging, not high-throughput event streaming.

Answer

Storing JSON documents for querying

Answer

Storing large binary objects for a website

Answer

Real-time event streaming for analytics

Question 4

A social media application stores user profile data as JSON documents. Each user's document has a different structure, with fields that vary based on user activity. The application needs to query these documents efficiently using SQL-like syntax and support high write throughput. Which Azure data store is most appropriate for this workload?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the most appropriate choice because it natively supports storing and querying JSON documents with varying schemas, offers SQL-like query syntax via its core (SQL) API, and provides guaranteed low-latency reads/writes at any scale with automatic indexing of all fields. Its multi-model nature and configurable consistency levels make it ideal for high-throughput workloads like a social media application.

Answer

Azure SQL Database

Answer

Azure Blob Storage

Answer

Azure Table Storage

Question 5

You need to choose a data storage solution for a global e-commerce platform that requires single-digit millisecond read and write latencies across multiple regions. The data is semi-structured and includes user profiles and product catalogs. Which Azure service should you use?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the correct choice because it is a globally distributed, multi-model database service that guarantees single-digit millisecond read and write latencies at the 99th percentile, regardless of the number of regions. It supports semi-structured data natively through its document (JSON) API, making it ideal for user profiles and product catalogs that require low-latency access across multiple geographic regions.

Answer

Azure Redis Cache

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Azure Table Storage

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Azure SQL Database

Question 6

A ride-sharing application uses Azure Cosmos DB for trip data. Each trip record contains TripID (unique), DriverID, RiderID, TripDate, and other details. The most common query retrieves all trips for a specific driver within a given date range. Which partition key should be chosen to minimize Request Unit (RU) consumption and ensure even data distribution?

Accepted Answer

DriverID. DriverID is the optimal partition key because the most common query filters on DriverID and a date range. Partitioning by DriverID ensures that all trips for a specific driver are stored in the same physical partition, making the query a single-partition operation that consumes minimal Request Units (RUs). It also provides even data distribution across partitions because each driver generates a roughly similar number of trips, avoiding hot spots.

Answer

TripID

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TripDate

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RiderID

Question 7

A data lake stores Parquet files in Azure Data Lake Storage Gen2, organized by date (e.g., /data/2023/01/15/). Analysts frequently run queries that filter on a specific date range. Which feature of Azure Data Lake Storage Gen2 directly enables efficient directory-level operations like renaming or moving entire date partitions without rewriting files?

Accepted Answer

Hierarchical namespace. The hierarchical namespace feature in Azure Data Lake Storage Gen2 enables true directory-level operations, such as renaming or moving entire partitions (e.g., /data/2023/01/15/), by treating directories as first-class objects. This allows atomic metadata operations without rewriting or copying the underlying Parquet files, which is essential for efficient partition management in data lake scenarios.

Answer

Blob soft delete

Answer

Change feed

Answer

Immutable storage

Question 8

Which TWO are benefits of using a NoSQL database like Azure Cosmos DB? (Choose two.)

Accepted Answer

Horizontal scalability. Option C (Horizontal scalability) is correct because Azure Cosmos DB is designed to scale out by partitioning data across many physical nodes, allowing throughput and storage to grow elastically without vertical hardware upgrades. Option D (Schema flexibility) is correct because NoSQL databases like Cosmos DB store JSON documents whose properties can vary per item, so the data model can evolve without migrations or a fixed table schema. Option A is incorrect because enforcing referential integrity is a hallmark of relational databases with foreign keys, which Cosmos DB does not enforce across containers. Option B is incorrect because complex multi-entity joins are a relational strength; Cosmos DB favors denormalization and embedded documents rather than SQL-style joins. Option E is incorrect as a general NoSQL benefit because full multi-document ACID transactions are not universal to NoSQL, and Cosmos DB only offers them within a single logical partition, so it is not a defining benefit of the category.

Answer

Enforcing referential integrity

Answer

Support for complex joins

Answer

Full ACID transactions across multiple documents

Question 9

A marketing team needs to analyze customer purchase history data stored in Azure SQL Database. They want to create interactive dashboards with drill-down capabilities. Which Microsoft tool should they use?

Accepted Answer

Power BI. Power BI is the correct tool because it is designed specifically for creating interactive dashboards with drill-down capabilities using data from Azure SQL Database. It connects directly to Azure SQL Database via built-in connectors, allowing users to build visualizations that support hierarchical navigation and real-time filtering.

Answer

Azure Data Studio

Answer

Microsoft Excel

Answer

Azure Analysis Services

Question 10

A multinational corporation is deploying a global application using Azure SQL Database. They need to ensure that users in different geographic regions experience low latency reads. The application can tolerate slightly stale data for reads, but writes must be strongly consistent and must occur in a single primary region. Which feature should they implement?

Accepted Answer

Active geo-replication. Active geo-replication in Azure SQL Database allows up to four readable secondary databases in different Azure regions, providing low-latency reads for geographically distributed users while maintaining a single writable primary. Because the application tolerates slightly stale reads, the asynchronous replication of active geo-replication is acceptable, and writes remain strongly consistent on the single primary. This matches the requirement exactly.

Answer

Azure Cosmos DB with multi-master

Answer

Failover groups

Answer

Read scale-out

Question 11

A company processes sales transactions in real-time from a retail website. Each transaction is recorded as a row in a relational database. Additionally, the company stores weekly sales reports as PDF files. Which statement correctly describes these data types?

Accepted Answer

Transactions are structured, reports are unstructured.. Transactions are structured because they are stored as rows in a relational database, which imposes a fixed schema with defined columns and data types. Weekly sales reports as PDF files are unstructured because they lack a predefined data model and cannot be easily queried using SQL without additional processing. Option B correctly identifies this distinction.

Answer

Transactions are unstructured, reports are semi-structured.

Answer

Both are structured because they are files.

Answer

Both are unstructured because they are digital.

Question 12

You are designing a multi-tenant SaaS application using Azure SQL Database. Each tenant has its own database. You need to perform maintenance across all databases efficiently. Which feature should you use?

Accepted Answer

Elastic Jobs. Elastic Jobs is the Azure SQL Database feature designed to run T-SQL scripts or maintenance tasks across many databases — including all databases in a pool or all databases on a server — from a single job definition. For a multi-tenant SaaS model where each tenant has its own database, Elastic Jobs lets you schedule and execute maintenance (index rebuilds, statistics updates, schema changes) across the entire fleet without writing custom orchestration.

Answer

Elastic pools

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Failover groups

Answer

SQL Server Agent

Question 13

A company stores IoT sensor data in Azure Blob Storage. The data is structured as JSON files organized by date. Data scientists need to query this data using SQL statements without moving it. Which Azure service should they use to enable this?

Accepted Answer

Azure Synapse Serverless SQL. Azure Synapse Serverless SQL pool lets data scientists query JSON files in Azure Blob Storage using T-SQL without moving or loading the data. It uses OPENROWSET with a data source pointing to the blob container, enabling ad-hoc SQL queries directly over the files.

Answer

Azure SQL Database

Answer

Azure Data Lake Storage Gen2

Answer

Azure Cosmos DB

Question 14

Which THREE data storage considerations are important when choosing between Azure SQL Database and Azure Cosmos DB? (Choose three.)

Accepted Answer

ACID transaction support. ACID transaction support (A) is a key consideration because Azure SQL Database provides full ACID transactions across relational data, while Azure Cosmos DB offers ACID transactions scoped to a logical partition, so the required transaction scope directly influences the choice. Global distribution capabilities (B) matter because Cosmos DB is designed for turnkey multi-region writes and reads with tunable consistency levels, whereas Azure SQL Database uses geo-replication and failover groups with a more limited consistency model. Schema flexibility (C) is important because Cosmos DB is schema-agnostic and stores JSON documents without predefined schemas, while Azure SQL Database enforces a fixed relational schema with tables, columns, and data types. Maximum storage capacity (D) is not a primary differentiator since both services scale to very large datasets and storage limits are configuration-dependent rather than architectural decision points. Built-in analytics features (E) are also not a core selection criterion because analytics in both platforms typically rely on external services such as Azure Synapse Analytics or Synapse Link rather than being a defining storage characteristic.

Answer

Maximum storage capacity

Answer

Built-in analytics features

Question 15

Which TWO of the following are valid Azure data storage services for storing unstructured data?

Accepted Answer

Azure Blob Storage. Azure Blob Storage (C) is correct because it is Microsoft's core object storage service designed specifically for massive amounts of unstructured data such as text, binary, images, video, and backups, accessible via REST/HTTPS and the Blob SDK. Azure Data Lake Storage Gen2 (D) is correct because it is built on Blob Storage with a hierarchical namespace enabled, purpose-built for storing and analyzing unstructured and semi-structured big data (e.g., logs, JSON, Parquet) at scale. Azure SQL Database (A) is a relational PaaS engine that stores structured data in tables with a fixed schema, so it is not intended for unstructured data. Azure Table Storage (B) is a NoSQL key-attribute store for structured, schema-less tabular data, not for unstructured blobs. Azure Cosmos DB (E) is a globally distributed multi-model database for structured/semi-structured JSON documents, key-value, graph, and column-family data, not a general unstructured object store.

Answer

Azure SQL Database

Answer

Azure Table Storage

Answer

Azure Cosmos DB

Question 16

Which TWO tools can be used to transform data in an Azure data pipeline?

Accepted Answer

Azure Databricks notebooks. Azure Databricks notebooks (C) are correct because they run Apache Spark code (Python, Scala, SQL, R) that can perform arbitrary data transformations such as filtering, joining, aggregating, and cleansing at scale within an Azure data pipeline. Azure Data Factory Data Flow (E) is correct because it provides a visual, code-free way to build scalable data transformation logic (mapping data flows) that executes on Azure Databricks-backed Spark clusters as part of an ADF pipeline. Power BI Desktop (A) is a reporting and visualization tool, not a pipeline data transformation service. Microsoft Purview (B) is a data governance, cataloging, and lineage service rather than a transformation engine. Azure Storage Explorer (D) is a client utility for browsing and managing storage accounts, not for transforming data.

Answer

Power BI Desktop

Answer

Microsoft Purview

Answer

Azure Storage Explorer

Question 17

Which TWO Azure services are appropriate for real-time data ingestion from IoT devices?

Accepted Answer

Azure IoT Hub. Azure IoT Hub (A) is correct because it is a fully managed, cloud-hosted service designed specifically for bidirectional communication with IoT devices, supporting device-to-cloud telemetry ingestion over protocols like MQTT, AMQP, and HTTPS at scale. Azure Event Hubs (E) is correct because it is a big-data streaming platform and event ingestion service capable of receiving millions of events per second in real time, commonly used as the ingestion endpoint for IoT telemetry pipelines. Azure Data Factory (B) is a batch-oriented data integration and orchestration service, not a real-time ingestion endpoint for devices. Azure SQL Database (C) is a relational database for storing and querying structured data, not a device ingestion service. Azure Blob Storage (D) is object storage for persisting data, not a real-time ingestion mechanism for IoT devices.

Answer

Azure Data Factory

Answer

Azure SQL Database

Answer

Azure Blob Storage

Question 18

Which TWO options are valid ways to ingest data into Azure Data Lake Storage Gen2?

Accepted Answer

AzCopy. AzCopy (C) is a valid ingestion method because it is a command-line utility designed to copy data to and from Azure Storage, including Data Lake Storage Gen2, using commands like `azcopy copy` with blob or ADLS Gen2 endpoints. Azure Data Factory (E) is also correct because it provides connectors and copy activities that can load data from numerous sources into ADLS Gen2 as a sink. Power BI (A) is primarily a visualization and reporting service, not a data ingestion tool for ADLS Gen2. Azure SQL Database (B) is a relational database service and can be a source or target, but it is not itself a valid ingestion mechanism into ADLS Gen2. Azure DevOps (D) is a development lifecycle platform and does not provide native data ingestion into ADLS Gen2.

Answer

Power BI

Answer

Azure SQL Database

Answer

Azure DevOps

Question 19

Which TWO Azure services can be used to perform real-time data ingestion and processing? (Choose two.)

Accepted Answer

Azure Event Hubs. Azure Event Hubs (B) is correct because it is a fully managed, real-time data ingestion service designed to receive millions of events per second from sources such as applications, IoT devices, and logs, buffering them for downstream stream processing. Azure Stream Analytics (E) is correct because it is a real-time stream processing engine that ingests data from sources like Event Hubs or IoT Hub and runs continuous SQL-like queries over the data to produce immediate insights, making it a core service for real-time processing. Azure SQL Database (A) is a relational database service intended for transactional storage and querying, not real-time event ingestion or stream processing. Azure Blob Storage (C) is scalable object storage for batch and archival data rather than a real-time ingestion/processing engine. Azure Data Factory (D) is a data integration and orchestration service primarily used for scheduled or triggered batch data movement and transformation, not real-time stream processing.

Answer

Azure SQL Database

Answer

Azure Blob Storage

Answer

Azure Data Factory

Question 20

Which TWO Azure services are primarily used for batch processing of large data volumes?

Accepted Answer

Azure Databricks. Azure Databricks (A) is correct because it is an Apache Spark-based analytics platform that natively supports distributed batch processing of large data volumes through Spark jobs, notebooks, and clusters. Azure Synapse Pipelines (C) is correct because it provides orchestration and data integration capabilities (built on Azure Data Factory) specifically designed to run batch data movement and transformation workloads at scale across large datasets. Azure Data Lake Storage (B) is not correct because it is a storage layer, not a processing service, even though it commonly stores data consumed by batch jobs. Azure Stream Analytics (D) is not correct because it is designed for real-time streaming analytics on continuous data streams rather than batch processing. Azure Logic Apps (E) is not correct because it is a workflow automation and integration service for orchestrating apps, not a large-scale batch data processing engine.

Answer

Azure Data Lake Storage

Answer

Azure Stream Analytics

Answer

Azure Logic Apps