DP-900 Describe an analytics workload on Azure Practice Test 4 — 15 Questions

Question 1

A data analytics team stores sales transaction data in Parquet files in Azure Data Lake Storage Gen2. They want to run complex analytical queries that join this data with dimension tables stored in Azure Synapse Analytics dedicated SQL pool. The team prefers not to move or copy the data from the data lake. Which feature should they use to query the data lake data directly?

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

Accepted Answer

PolyBase external tables. PolyBase external tables in Azure Synapse Analytics dedicated SQL pool allow you to query data stored in Azure Data Lake Storage Gen2 (ADLS Gen2) directly using T-SQL, without moving or copying the data. This is the correct feature because it enables complex analytical joins between the Parquet files in the data lake and the dimension tables in the dedicated SQL pool, leveraging the external table's ability to read Parquet format natively.

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Azure Data Factory pipelines

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Azure Stream Analytics

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Azure Databricks notebooks

Question 2

A healthcare analytics company receives continuous streams of patient monitoring data from IoT devices. The data must be processed in near real-time to detect critical events (e.g., abnormal heart rate). Processed data is then stored in a columnar format for historical analysis and reporting by data analysts using SQL. Which combination of Azure services should they use for ingestion, processing, and storage?

Accepted Answer

Azure Event Hubs, Azure Stream Analytics, Azure Synapse Analytics. Azure Event Hubs is designed for high-throughput, low-latency ingestion of streaming data from millions of IoT devices. Azure Stream Analytics provides a SQL-based, near real-time processing engine to detect critical events like abnormal heart rates. Azure Synapse Analytics (formerly SQL Data Warehouse) offers a columnar storage format (e.g., columnstore indexes) optimized for historical analysis and SQL-based reporting by data analysts.

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Azure IoT Hub, Azure Data Factory, Azure SQL Data Warehouse

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Azure Event Hubs, Azure Stream Analytics, Azure Cosmos DB

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Azure Blob Storage, Azure Databricks, Azure Table Storage

Question 3

A retail chain collects daily sales data from hundreds of stores. The data is stored as CSV files in Azure Data Lake Storage Gen2. The analytics team needs to run complex SQL queries that join sales data with product dimensions and aggregate results across petabytes of data. Queries must return results within seconds. Which Azure service is best suited for this analytical workload?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics (formerly SQL Data Warehouse) is the correct choice because it is a distributed query engine designed for petabyte-scale data warehousing. It uses Massively Parallel Processing (MPP) to distribute data across compute nodes, enabling complex SQL joins and aggregations on data stored in Azure Data Lake Storage Gen2 to return results in seconds via its SQL pool or serverless SQL endpoint.

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Azure SQL Database

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Azure Analysis Services

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Azure HDInsight

Question 4

A financial analytics company has petabytes of transaction data stored as Parquet files in Azure Data Lake Storage Gen2. Data analysts need to run complex SQL queries that join multiple tables and return results within seconds. The company wants to query the data directly without moving it to another store. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is correct because it allows querying data directly from Azure Data Lake Storage Gen2 using T-SQL without moving or loading the data. It uses a distributed query engine that can process petabytes of Parquet files and return results in seconds by leveraging pushdown computation and columnar storage formats.

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Azure SQL Database

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Azure HDInsight

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Azure Databricks

Question 5

A retail company analyzes customer purchase patterns. Every night, they run a batch job that aggregates millions of transactions from the past day into summary tables for reporting. Which type of data processing workload best describes this nightly job?

Accepted Answer

Batch processing. This nightly job processes a large volume of transactions accumulated over the past day in a single, scheduled run, which is the defining characteristic of batch processing. In Azure, this workload would typically be implemented using Azure Synapse Analytics or Azure Data Factory to orchestrate the aggregation of millions of rows into summary tables for reporting, without requiring immediate output.

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Real-time processing

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Streaming processing

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Transactional processing

Question 6

A retail chain captures real-time sales data from point-of-sale (POS) systems as a stream of events. The data is ingested into Azure Event Hubs. Additionally, the company receives daily inventory files in CSV format uploaded to Azure Data Lake Storage Gen2. The analytics team needs to combine the streaming sales data with the batch inventory data to generate near real-time dashboards and run historical reports. They want a single analytics platform that can handle both streaming and batch workloads, and allow querying data directly in the data lake using SQL. Which Azure service should they choose?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that natively integrates with Azure Event Hubs for real-time streaming ingestion and Azure Data Lake Storage Gen2 for batch data. Its Synapse SQL engine supports querying data directly in the data lake using T-SQL, enabling near real-time dashboards and historical reports without data movement. This service is designed to handle both streaming and batch workloads in a single workspace, meeting all the stated requirements.

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Azure Stream Analytics

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Azure Data Lake Analytics

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Azure HDInsight

Question 7

A marketing team wants to analyze social media sentiment in near real-time. They will use Azure Event Hubs to capture tweets and need to aggregate sentiment scores over 5-minute windows. The aggregated results must be stored in Azure Blob Storage for later analysis. Which Azure service should they use to perform the stream processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is a real-time stream processing engine designed to ingest data from sources like Azure Event Hubs, apply temporal aggregations (e.g., 5-minute tumbling windows), and output results directly to Azure Blob Storage. It provides built-in support for windowed functions and exactly-once delivery semantics, making it ideal for near-real-time sentiment analysis without requiring custom code.

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Azure Data Factory

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Azure Databricks

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Azure Synapse Analytics

Question 8

A company is migrating their on-premises data warehouse, which is built on a Netezza appliance, to Azure. The data warehouse contains over 10 terabytes of data and supports complex BI queries with multiple joins and aggregations. The company requires a cloud-based solution that provides massively parallel processing (MPP) to handle large-scale queries efficiently. They also need to integrate with existing ETL tools like Azure Data Factory and provide native connectivity to Power BI. Which Azure service should they choose?

Accepted Answer

Azure Synapse Analytics dedicated SQL pool. Azure Synapse Analytics dedicated SQL pool is the correct choice because it provides massively parallel processing (MPP) architecture designed for petabyte-scale data warehousing, exactly matching the 10+ TB requirement. It natively integrates with Azure Data Factory for ETL and offers built-in Power BI connectivity via the T-SQL endpoint, supporting complex BI queries with multiple joins and aggregations.

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Azure SQL Database

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Azure Databricks

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Azure HDInsight

Question 9

A manufacturing company collects sensor data from factory equipment as a continuous stream of events ingested into Azure Event Hubs. Additionally, the company receives daily inventory CSV files uploaded to Azure Data Lake Storage Gen2. The analytics team needs to build near real-time dashboards that combine streaming sensor data with batch inventory data, and also support historical reporting by querying data directly in the data lake using SQL without moving it. Which Azure service should they choose 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 both real-time streaming data from Azure Event Hubs and batch data from Azure Data Lake Storage Gen2. Its SQL Serverless feature allows querying data directly in the data lake using T-SQL without moving it, enabling near real-time dashboards and historical reporting in a single service.

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Azure Stream Analytics

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Azure Data Factory

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Azure HDInsight with Spark

Question 10

A retail company runs a nightly process that reads all sales transactions from the previous day, aggregates them by product category and store location, and writes the summary results into a data warehouse for reporting. Which type of data processing workload best describes this nightly process?

Accepted Answer

Batch processing. The nightly process reads all sales transactions from the previous day, aggregates them, and writes summary results into a data warehouse. This is a classic batch processing workload because data is collected over a period (the entire previous day), processed in a single offline job, and the output is stored for later reporting. Batch processing is ideal for high-volume, non-real-time transformations like nightly ETL (Extract, Transform, Load) jobs.

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Online Transaction Processing (OLTP)

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Stream processing

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Data warehousing

Question 11

A financial analytics company stores petabytes of transaction data in Parquet files in Azure Data Lake Storage Gen2. Data analysts need to run complex SQL queries that join multiple large tables and return results within seconds. The company also wants to integrate with Power BI for visualization and Azure Data Factory for ETL orchestration. They require a massively parallel processing (MPP) engine to handle the scale. Which Azure service should they choose?

Accepted Answer

Azure Synapse Analytics dedicated SQL pool. Azure Synapse Analytics dedicated SQL pool is the correct choice because it provides a massively parallel processing (MPP) engine that distributes data across 60 distributions, enabling fast execution of complex SQL queries on petabyte-scale data stored in Parquet files in Azure Data Lake Storage Gen2. It natively integrates with Power BI for visualization and Azure Data Factory for ETL orchestration, meeting all stated requirements.

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Azure SQL Database

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Azure Cosmos DB

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Azure Analysis Services

Question 12

A retail company runs a nightly job that reads all sales transactions from the previous day from an operational database, aggregates them by product category and store location, and writes the summary results into a data warehouse for reporting. Which type of data processing workload does this nightly job represent?

Accepted Answer

B. Batch processing. The nightly job processes data in discrete, scheduled batches—reading all sales transactions from the previous day, aggregating them, and writing results to a data warehouse. This is the classic definition of batch processing, where data is collected over a period and processed together in a single job run. In Azure, this workload maps to services like Azure Data Factory or Azure Synapse Pipelines executing scheduled pipelines.

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A. Real-time processing

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C. Stream processing

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D. Transactional processing

Question 13

A financial services company stores years of market trade data as Parquet files in Azure Data Lake Storage Gen2. The data volume is terabytes and growing rapidly. Data analysts need to run complex SQL queries that join multiple tables (e.g., trades, instruments, counterparties) and return results within seconds. The company also wants to integrate with Power BI for visualization and Azure Data Factory for orchestration of ETL pipelines. Which Azure service should they choose as the primary analytics platform?

Accepted Answer

Azure Synapse Analytics (serverless SQL pool). Azure Synapse Analytics serverless SQL pool is the correct choice because it provides a distributed SQL query engine that can directly query Parquet files in Azure Data Lake Storage Gen2 using T-SQL, enabling complex joins across multiple tables with fast performance via automatic query optimization and pushdown computation. It integrates natively with Power BI for visualization and Azure Data Factory for ETL orchestration, making it the ideal primary analytics platform for large-scale, schema-on-read data lake scenarios.

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Azure SQL Database

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Azure HDInsight with Spark

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Azure Analysis Services

Question 14

A manufacturing company ingests a continuous stream of sensor data from factory equipment into Azure Event Hubs. Additionally, historical maintenance data in CSV format is stored in Azure Data Lake Storage Gen2. The analytics team needs to join the streaming sensor data with the historical data in near real-time and enable analysts to query the combined dataset using standard T-SQL without moving the data. Which Azure service should they use as the primary analytics platform?

Accepted Answer

B) Azure Synapse Analytics with Synapse Pipelines and serverless SQL pool. Azure Synapse Analytics with Synapse Pipelines and serverless SQL pool is the correct choice because it can ingest streaming data from Event Hubs via pipelines, query historical CSV data in Data Lake Storage Gen2 directly using T-SQL without moving it, and join both datasets in near real-time using the serverless SQL pool's ability to reference external data sources. This meets the requirement for standard T-SQL queries on combined streaming and historical data without data movement.

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A) Azure Stream Analytics

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C) Azure SQL Database

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D) Azure Databricks

Question 15

A manufacturing company connects thousands of IoT sensors on an assembly line, each sending telemetry data every second. The data volume is terabyte-scale per day. The company needs to analyze the sensor data in near real-time to detect anomalies (e.g., temperature spikes) and also allow data scientists to run interactive ad-hoc queries on the historical data to find patterns. They prefer using a query language similar to SQL. Which Azure service should they choose?

Accepted Answer

Azure Data Explorer. Azure Data Explorer (ADX) is designed for high-velocity telemetry data, ingesting terabytes per day from IoT sensors with sub-second latency. It supports Kusto Query Language (KQL), which is SQL-like and optimized for time-series analysis, anomaly detection, and interactive ad-hoc queries on both real-time and historical data. This makes it the ideal choice for the described scenario.

Answer

Azure Stream Analytics

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Azure Synapse Analytics dedicated SQL pool

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Azure Databricks with Structured Streaming