20+ practice questions focused on Describe an analytics workload on Azure — one of the most tested topics on the Microsoft Azure Data Fundamentals DP-900 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Describe an analytics workload on Azure PracticeA manufacturer collects sensor data from thousands of IoT devices every second. The data is ingested into Azure Event Hubs and then needs to be stored for historical analysis. The analytics team will run complex aggregations and time-series queries over petabytes of data, expecting fast results even with large scans. Which Azure service should be used as the analytical data store?
Explanation: Azure Synapse Analytics dedicated SQL pool is the correct choice because it is a massively parallel processing (MPP) engine designed for petabyte-scale data warehousing. It can run complex aggregations and time-series queries with fast results by distributing data across 60 distributions and using columnstore indexes for high compression and scan efficiency.
A manufacturing company has a streaming data pipeline that ingests sensor data from factory equipment into Azure Event Hubs. The data must be prepared for reporting by cleaning invalid records, removing duplicates, and aggregating readings into 5-minute windows. The transformed data needs to be stored in a columnar format in a data lake to support efficient querying by data analysts using SQL. Which Azure service should perform the data transformation and loading?
Explanation: Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, directly consuming data from Azure Event Hubs, performing transformations like cleaning invalid records, removing duplicates, and aggregating over tumbling windows (e.g., 5-minute windows), and outputting the results in a columnar format (e.g., Parquet) to Azure Data Lake Storage. This aligns perfectly with the requirement for a low-latency, continuous transformation pipeline without needing additional orchestration or compute clusters.
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?
Explanation: 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.
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?
Explanation: 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.
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?
Explanation: 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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