DP-900 Practice Test 37 — 15 Questions

Question 1

A retail company ingests clickstream data from its e-commerce website into Azure Event Hubs. They need to detect customer journey patterns in real time within seconds and also prepare aggregated data for daily trend reports stored in Azure Data Lake Storage Gen2. The real-time processing must handle high throughput and support complex temporal queries like sessionization. The daily aggregation should be cost-effective and use serverless compute. Which combination of Azure services should they use?

Accepted Answer

Azure Stream Analytics for real-time processing and Azure Data Factory for daily batch aggregation. Azure Stream Analytics is ideal for real-time processing of high-throughput clickstream data from Event Hubs, supporting complex temporal queries like sessionization with low latency (seconds). Azure Data Factory provides cost-effective, serverless orchestration for daily batch aggregation, efficiently moving and transforming data to Azure Data Lake Storage Gen2 without managing infrastructure.

Answer

Azure Functions for real-time processing and Azure Databricks for daily batch aggregation

Answer

Azure Stream Analytics for real-time processing and Azure Batch for daily batch aggregation

Answer

Azure Data Lake Analytics for real-time processing and Azure Data Factory for daily batch aggregation

Question 2

A manufacturing company collects temperature and vibration data from thousands of sensors. The data is streamed to Azure Event Hubs. The company wants to store all this raw data in Azure Data Lake Storage Gen2 for future batch analytics. They need a solution that automatically writes the streaming data to the data lake in near real-time, without requiring any custom code for the write operation. Which Azure feature should they use?

Accepted Answer

Azure Event Hubs Capture. Azure Event Hubs Capture is the correct choice because it automatically writes streaming data from Event Hubs to Azure Data Lake Storage Gen2 in near real-time without requiring any custom code. It integrates directly with Event Hubs to buffer and write data in Avro format, meeting the requirement for a no-code, automated solution.

Answer

Azure Stream Analytics job output to Azure Data Lake Storage Gen2

Answer

Azure Data Factory Copy Activity

Answer

Azure Synapse Pipelines

Question 3

A retail company processes petabytes of sales transaction data stored in Azure Data Lake Storage Gen2. They need to run recurring complex queries that involve large joins and aggregations. The queries must consistently complete within a fixed time window overnight. The company wants predictable performance and costs. Which Azure service should they use?

Accepted Answer

Azure Synapse Analytics Dedicated SQL pool. Azure Synapse Analytics Dedicated SQL pool provides reserved, fixed compute resources that ensure predictable performance and cost for recurring complex queries involving large joins and aggregations. It is designed for petabyte-scale data warehousing workloads with consistent SLAs, making it ideal for overnight batch processing within a fixed time window.

Answer

Azure Synapse Analytics Serverless SQL pool

Answer

Azure SQL Database

Answer

Azure Analysis Services

Question 4

A logistics company receives real-time GPS tracking data from its delivery fleet via Azure Event Hubs. The data is a continuous stream of location updates (vehicle ID, latitude, longitude, timestamp). Additionally, the company has daily static route plan files in CSV format stored in Azure Data Lake Storage Gen2. The operations team needs to combine the live GPS stream with the route plans to create a near real-time dashboard showing if delivery vehicles are on schedule. They also want to run historical queries on both the stream data and route plans using T-SQL, without moving the data to another store. Which Azure service should they use as the primary analytics platform?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that can ingest real-time streaming data from Azure Event Hubs and combine it with static data in Azure Data Lake Storage Gen2. It supports T-SQL queries directly against both the live stream (via Synapse Pipelines or SQL Serverless) and the CSV files in the lake, enabling near real-time dashboards and historical analysis without moving data to another store.

Answer

Azure Stream Analytics

Answer

Azure Data Factory

Answer

Azure Databricks

Question 5

An administrator runs an Azure CLI command to update an Azure SQL Database's service objective. The output shows the above properties. The database is currently at S2 and the administrator wants to scale to S3. What is the issue?

Accepted Answer

The command did not specify the new service objective. Option A is correct because the properties show the current service objective but the command likely did not change it. The requestedServiceObjectiveName is still S2, meaning the update was not applied. Option B is wrong because maxSizeBytes is within range for S2. Option C is wrong because the property names are correct. Option D is wrong because the CLI command can update service objective.

Answer

The property names are incorrect

Answer

The maxSizeBytes value is invalid

Answer

The Azure CLI does not support scaling Azure SQL Database

Question 6

A retail company stores historical sales data from multiple stores in Azure Data Lake Storage Gen2 as CSV files. They need to run complex SQL queries that join and aggregate data across multiple files to generate weekly sales reports. They want a serverless query service that can directly query the data in the lake without loading it into a separate database. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is the correct choice because it provides a serverless, on-demand SQL query engine that can directly query CSV files stored in Azure Data Lake Storage Gen2 using T-SQL syntax. It supports complex joins and aggregations across multiple files without requiring data movement or loading into a separate database, making it ideal for ad-hoc reporting on data lakes.

Answer

Azure SQL Database

Answer

Azure Stream Analytics

Answer

Azure Data Factory

Question 7

A smart building monitoring company ingests real-time sensor data (temperature, humidity, occupancy) from thousands of IoT devices into Azure Event Hubs. The company also stores historical building blueprints and maintenance records as CSV files in Azure Data Lake Storage Gen2. The engineering team needs to build a dashboard that displays live sensor readings overlaid on building floor plans, and also allows facility managers to run ad-hoc T-SQL queries that combine live sensor data with historical maintenance records. Which Azure service should they use as the primary analytics platform to meet both requirements?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that combines big data and data warehousing. It can ingest real-time streaming data from Azure Event Hubs via its built-in Spark pools or pipelines, and also run ad-hoc T-SQL queries against both the live sensor data (staged in tables) and historical CSV files stored in Azure Data Lake Storage Gen2 using serverless SQL pools. This meets both the real-time dashboard and ad-hoc T-SQL query requirements.

Answer

Azure Stream Analytics

Answer

Azure Databricks

Answer

Azure Analysis Services

Question 8

A transportation company collects real-time GPS data from thousands of delivery vehicles. They need to process this streaming data to detect delays and generate alerts when a vehicle is behind schedule. Which Azure service should they use for the stream processing?

Accepted Answer

B. Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is a fully managed, real-time stream processing engine designed to handle high-velocity data from sources like IoT devices and GPS sensors. It can ingest streaming data from Azure Event Hubs or IoT Hub, apply SQL-based queries to detect patterns such as delays, and output alerts to sinks like Azure Functions or Power BI in near real-time.

Answer

A. Azure Data Factory

Answer

C. Azure Data Lake Analytics

Answer

D. Azure Analysis Services

Question 9

A telecommunications company needs to analyze call detail records (CDRs) to detect fraud patterns and minimize revenue leakage. The data arrives as a continuous stream from network switches and must be queried within seconds of ingestion to flag suspicious activity. The analysts also need to run interactive ad-hoc queries over the last 90 days of CDR data using a Kusto query language. Which Azure service should they use as the primary data store and analytics engine?

Accepted Answer

Azure Data Explorer. Azure Data Explorer is optimized for high-velocity telemetry data like CDRs, supporting ingestion of continuous streams with sub-second query latency. Its native Kusto Query Language (KQL) enables both real-time fraud detection and interactive ad-hoc queries over large time windows (e.g., 90 days) without pre-aggregation or indexing overhead.

Answer

Azure Synapse Analytics

Answer

Azure Stream Analytics

Answer

Azure Analysis Services

Question 10

Which TWO features are available in Azure SQL Database to help protect data at rest?

Accepted Answer

Transparent Data Encryption (TDE). Option A and Option C are correct. Transparent Data Encryption encrypts the database files at rest. Always Encrypted encrypts sensitive columns at rest and in use. Option B is wrong because Dynamic Data Masking does not encrypt data; it masks it in query results. Option D is wrong because Auditing is for tracking, not encryption. Option E is wrong because Row-Level Security controls access, not encryption.

Answer

Dynamic Data Masking

Answer

Auditing

Answer

Row-Level Security

Question 11

A business analyst needs to explore and create interactive visualizations of sales data stored in Azure Data Lake Storage Gen2 without writing SQL code. Which Azure service is best suited for this drag-and-drop data exploration?

Accepted Answer

Microsoft Power BI. Microsoft Power BI is the correct choice because it provides a drag-and-drop interface for creating interactive visualizations and exploring data without requiring SQL code. It can directly connect to Azure Data Lake Storage Gen2 using Power Query or the ADLS connector, enabling business analysts to build reports and dashboards through intuitive visual interactions.

Answer

Azure Stream Analytics

Answer

Azure Data Factory

Answer

Azure Databricks

Question 12

A company has a data warehouse in Azure Synapse Analytics dedicated SQL pool. They need to load new sales data every night from a CSV file stored in Azure Data Lake Storage Gen2. The load process must be automated, scheduled, and have error handling for failed loads. Which Azure service should they use to orchestrate this process?

Accepted Answer

Azure Data Factory. Azure Data Factory is the correct choice because it is a cloud-based ETL service designed specifically for orchestrating and automating data movement and transformation at scale. It supports scheduled triggers, native connectors to Azure Data Lake Storage Gen2 and Azure Synapse Analytics, and built-in error handling via retry policies and failure activities, making it ideal for nightly CSV file loads.

Answer

Azure Stream Analytics

Answer

Azure Databricks

Answer

Azure Logic Apps

Question 13

A company is designing an enterprise analytics solution. They store raw data in its original format in a scalable repository, apply schema and transformations at read time, and also maintain a curated layer that enforces ACID transactions for data reliability. This architecture combines the flexibility of a data lake with the reliability of a data warehouse. Which term best describes this modern data architecture?

Accepted Answer

Data lakehouse. The data lakehouse architecture combines the flexibility of a data lake (storing raw data in its original format in a scalable repository) with the reliability of a data warehouse (enforcing ACID transactions in a curated layer). This allows schema-on-read transformations while maintaining data integrity, making it the correct term for the described design.

Answer

Data mart

Answer

Operational database

Answer

Data pipeline

Question 14

A business analyst needs to create interactive visualizations and share dashboards with colleagues using data stored in an Azure Synapse Analytics dedicated SQL pool. Which tool should the analyst use?

Accepted Answer

Power BI Desktop. Power BI Desktop is the correct tool because it is designed for creating interactive visualizations and dashboards, and it can connect directly to Azure Synapse Analytics dedicated SQL pools via the built-in Azure Synapse Analytics connector. This allows the business analyst to build reports and share them with colleagues through the Power BI service, meeting the requirement for interactive dashboards and collaboration.

Answer

Azure Data Studio

Answer

SQL Server Management Studio (SSMS)

Answer

Azure Machine Learning Studio

Question 15

Your team is designing a database for a financial application that requires ACID transactions. You are considering Azure Cosmos DB and Azure SQL Database. Which service should you choose?

Accepted Answer

Azure SQL Database. Option B is correct because Azure SQL Database provides full ACID transactions. Option A is wrong because Azure Cosmos DB provides limited transactional support. Option C is wrong because Azure Database for MySQL provides ACID but the question asks between Cosmos DB and SQL Database. Option D is wrong because Azure Table Storage does not support ACID.

Answer

Azure Table Storage

Answer

Azure Cosmos DB

Answer

Azure Database for MySQL