DP-900 Describe an analytics workload on Azure Practice Test 7 — 15 Questions

Question 1

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.

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A. Azure Data Factory

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C. Azure Data Lake Analytics

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D. Azure Analysis Services

Question 2

A data analyst needs to run ad-hoc SQL queries on large volumes of data stored as Parquet files in Azure Data Lake Storage Gen2. The queries are unpredictable, and the analyst wants to pay only for the compute resources consumed by each query. Which Azure Synapse Analytics compute model should be used?

Accepted Answer

Serverless SQL pool. Serverless SQL pool is the correct choice because it allows running ad-hoc SQL queries directly on data in Azure Data Lake Storage Gen2 without provisioning any fixed compute resources. It uses a pay-per-query billing model, charging only for the amount of data processed by each query, which aligns perfectly with the unpredictable query patterns described.

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Dedicated SQL pool

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Apache Spark pool

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Azure Data Explorer pool

Question 3

A manufacturing company installs temperature sensors in a factory. Sensor data is streamed to Azure Event Hubs. The company needs to detect when the average temperature of any sensor exceeds 100°F over a 5-minute sliding window and then send an alert. Which Azure service should be used for this real-time stream processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, including the ability to define a 5-minute sliding window over sensor data from Event Hubs, compute the average temperature per sensor, and trigger an alert when the threshold of 100°F is exceeded. It natively integrates with Event Hubs as an input and supports temporal window functions like TumblingWindow, HoppingWindow, and SlidingWindow for exactly this kind of time-based aggregation.

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Azure Functions

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Azure SQL Database

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Azure Logic Apps

Question 4

A data engineering team needs to build a pipeline that ingests streaming data from IoT devices into Azure Data Lake Storage Gen2. The data arrives as JSON messages. They want to use a service that can capture the streaming data in near real-time and store it as files in the data lake without writing custom code for the ingestion. Which Azure service should they use?

Accepted Answer

Azure Event Hubs with Capture. Azure Event Hubs with Capture is the correct choice because it natively ingests streaming JSON data from IoT devices in near real-time and automatically writes the data to Azure Data Lake Storage Gen2 as files without requiring any custom code. The Capture feature automatically persists the event stream to the specified storage destination at defined time or size intervals, making it ideal for serverless, code-free ingestion.

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Azure Data Factory

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Azure Stream Analytics

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Azure Synapse Pipelines

Question 5

A data engineering team needs to build a batch processing pipeline that transforms large volumes of sales data stored in Azure Data Lake Storage Gen2. The transformations include aggregations and joins, and the output should be stored back in the data lake as Parquet files. The team wants a serverless compute option that automatically scales and charges per second. Which Azure service should they use?

Accepted Answer

Azure Databricks with auto-scaling clusters. Azure Databricks with auto-scaling clusters is the correct choice because it provides a serverless, Apache Spark-based compute platform that automatically scales resources based on workload demand and charges per second (via serverless or low-concurrency modes). It is ideal for batch processing large volumes of data in Azure Data Lake Storage Gen2, supporting complex transformations like aggregations and joins, and can write output directly as Parquet files.

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Azure Synapse Analytics dedicated SQL pool

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Azure Data Factory with mapping data flows

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Azure Stream Analytics

Question 6

A company uses Azure Synapse Analytics dedicated SQL pool for its data warehouse. Every day, they need to incrementally load 100 GB of new sales data from CSV files stored in Azure Data Lake Storage Gen2 (ADLS Gen2). The load should use PolyBase for efficient parallel data transfer and must be orchestrated on a recurring schedule. Which Azure service should they use to create and manage this pipeline?

Accepted Answer

A: Azure Data Factory. Azure Data Factory (ADF) is the correct choice because it provides native orchestration and scheduling capabilities for data pipelines. It supports PolyBase as a sink to load data into Azure Synapse dedicated SQL pool in parallel, and it can directly read CSV files from ADLS Gen2. ADF's built-in triggers allow you to schedule the daily incremental load without additional coding.

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B: Azure Stream Analytics

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C: Azure Databricks

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D: Azure Logic Apps

Question 7

A data engineering team wants to build a batch analytics pipeline. The raw data is stored in Azure Data Lake Storage Gen2 (ADLS Gen2). The final output will be a set of tables in Azure Synapse Analytics (dedicated SQL pool) that will be used to create reports in Power BI. Arrange the following steps in the correct order for a typical ETL process.

Accepted Answer

Ingest raw data from ADLS Gen2 into Azure Databricks for transformation using Azure Data Factory pipelines. ETL follows the order: extract raw data to ADLS, transform it, load into Synapse staging, then transform within Synapse to final tables, and finally use Power BI for reporting.

Question 8

A data engineering team needs to build a real-time dashboard showing sales totals by region. Sales transactions are streamed from point-of-sale systems into Azure Event Hubs. The team wants to aggregate the data in near real-time (e.g., every minute) and store the results in Azure SQL Database for visualization in Power BI. Which Azure service should they use for the aggregation step?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, allowing you to define a query that aggregates sales data from Event Hubs over a one-minute tumbling window and output the results directly to Azure SQL Database. This meets the requirement for near real-time aggregation without needing to write custom code or manage infrastructure.

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Azure Data Factory

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Azure Synapse Pipelines

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Azure Logic Apps

Question 9

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.

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Azure Stream Analytics job output to Azure Data Lake Storage Gen2

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Azure Data Factory Copy Activity

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Azure Synapse Pipelines

Question 10

A data engineering team needs to build a batch ETL pipeline that transforms large volumes of clickstream data stored as CSV files in Azure Data Lake Storage Gen2. The transformations require running distributed Python and Scala code using Apache Spark. The transformed data will be loaded into a data warehouse for reporting. The team wants a serverless compute environment that automatically scales and charges per second. Which Azure service should they use to run the Spark transformations?

Accepted Answer

Azure Synapse Analytics (Spark pools). Azure Synapse Analytics (Spark pools) is the correct choice because it provides a serverless Apache Spark compute environment that automatically scales and charges per second, perfectly matching the requirement for running distributed Python and Scala transformations on large volumes of clickstream data stored in Azure Data Lake Storage Gen2. The service integrates directly with the data lake and can load transformed results into a dedicated SQL pool for data warehouse reporting.

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Azure Data Factory

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Azure Stream Analytics

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Azure Analysis Services

Question 11

A company uses Azure Synapse Analytics dedicated SQL pool as its data warehouse. New data is loaded into the warehouse every few minutes. The company wants to visualize the data with near real-time updates in a dashboard that can be refreshed automatically. Which tool and connection mode should they use?

Accepted Answer

Power BI with DirectQuery mode. Power BI with DirectQuery mode is correct because it allows the dashboard to query the Azure Synapse dedicated SQL pool directly for each visual refresh, enabling near real-time updates without importing data. This mode avoids the latency of data import and supports automatic page refresh, which aligns with the requirement for data loaded every few minutes.

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Azure Data Studio with visualizations

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SQL Server Reporting Services (SSRS) with cached reports

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Excel Power Pivot with imported data

Question 12

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.

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Azure SQL Database

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Azure Stream Analytics

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Azure Data Factory

Question 13

A data analyst needs to run ad-hoc SQL queries on large datasets stored as Parquet files in Azure Data Lake Storage Gen2. The queries are infrequent and the data volume varies. The analyst wants to pay only for the amount of data processed per query and does not want to manage any infrastructure. They also need to create views in T-SQL to simplify queries for Power BI reports. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is the correct choice because it allows running ad-hoc T-SQL queries directly on Parquet files in Azure Data Lake Storage Gen2 without provisioning any infrastructure. It uses a pay-per-query model, charging only for the amount of data processed, and supports creating T-SQL views that can be used directly by Power BI for simplified reporting.

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Azure Data Lake Analytics

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Azure HDInsight

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Azure Databricks

Question 14

A financial services company processes real-time stock trade data from multiple exchanges. Trades are ingested into Azure Event Hubs. The company needs to compute a 5-minute sliding window average of trade prices per stock symbol and ensure that each trade is processed exactly once within the window. The aggregated results must be stored in Azure SQL Database for historical reporting and also sent to a Power BI dashboard for near real-time visualization. Which Azure service should be used for the real-time processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is purpose-built for real-time stream processing with native support for time-based windowing (e.g., 5-minute sliding window) and exactly-once semantics when used with Azure Event Hubs as input and Azure SQL Database as output. It can directly compute the sliding window average of trade prices per stock symbol and output results to both Azure SQL Database for historical storage and Power BI for near real-time visualization, all without requiring additional code or infrastructure management.

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Azure Databricks with Structured Streaming

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Azure Data Factory

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Azure Event Hubs

Question 15

A company needs to ingest data from an on-premises SQL Server database into Azure SQL Database every hour. During the ingestion, they need to filter out rows where Status = 'Inactive' and convert a date column to a different format. They want a cloud-based, code-free solution that can schedule and orchestrate this task. Which Azure service should they use?

Accepted Answer

Azure Data Factory with Mapping Data Flows. Azure Data Factory with Mapping Data Flows is the correct choice because it provides a cloud-based, code-free ETL service that can ingest data from on-premises SQL Server into Azure SQL Database, apply transformations like filtering rows (Status = 'Inactive') and converting date formats, and schedule the task using triggers. Mapping Data Flows run on Spark clusters and allow visual data transformation without writing code, making it ideal for this orchestrated, scheduled ingestion.

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Azure Logic Apps

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Azure Functions

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Azure SQL Database Change Data Capture