DP-900 Describe an analytics workload on Azure Practice Question
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?
⚠ Common exam trap
Test-takers frequently choose Azure Stream Analytics because it handles streaming, but they overlook the requirement for a single platform that also supports batch data and direct SQL querying of the data lake, which Stream Analytics cannot do for historical reports.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Azure Synapse Analytics
Why this is correct
Azure Synapse Analytics is the correct choice because it is a unified analytics service that natively combines both real-time stream ingestion (via pipelines, Spark Structured Streaming, or Azure Stream Analytics jobs attached to a Synapse workspace) and scalable batch processing, all while enabling serverless SQL or dedicated SQL pools to query files directly in the data lake. This means the retail chain can land streaming POS data into Parquet/Delta files and immediately query those same files with T-SQL, without stitching together separate services. Its integrated architecture is specifically designed to handle exactly this mixed workload pattern.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is incorrect because it is a specialized real-time event-processing engine that works only on streaming inputs (like Event Hubs or IoT Hub) and produces continuous outputs; it is not designed to handle batch processing of daily inventory files or to query existing data lake files directly with standard SQL. While it could process the POS stream, the retail chain also needs batch analytics and ad-hoc SQL over the lake, which Stream Analytics cannot deliver. You would still need a separate batch service, so it does not meet the 'unified platform' requirement in the scenario.
When this WOULD be correct
If the question required only real-time processing of streaming data from Event Hubs, with output to a sink like Power BI or storage, and no need to combine with batch data or query data lake files directly, Azure Stream Analytics would be the correct choice.
- ✗
Azure Data Lake Analytics
Why it's wrong here
Azure Data Lake Analytics is incorrect because it is a batch-only, on-demand analytics service that processes data using U-SQL scripts. It has no built-in stream processing engine, so it cannot ingest or analyze real-time point-of-sale events as they occur. Additionally, it does not provide a SQL query interface over files in the way Synapse serverless SQL does, and Microsoft has deprecated it in favor of Azure Synapse Analytics, making it unsuitable for a modern, unified real-time and batch solution.
When this WOULD be correct
A company needs to run complex, custom U-SQL scripts over massive datasets stored in Azure Data Lake Storage, with no requirement for real-time streaming or interactive SQL querying. The focus is on scalable batch processing for data transformation and analytics.
- ✗
Azure HDInsight
Why it's wrong here
Incorrect. Azure HDInsight is a managed Hadoop/Spark cluster that can handle both batch and stream processing, but it is not a single unified platform for SQL querying of data lake files; it requires more manual setup and management compared to Synapse.
When this WOULD be correct
A company needs to run custom big data processing jobs (e.g., using Spark, Hive, or MapReduce) on large datasets in Azure Storage, and requires full control over cluster configuration, libraries, and scaling. The question would specify that the team needs to run custom code (e.g., Python, Scala) for complex transformations, not just SQL queries.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Azure Synapse AnalyticsCorrect answer▾
Why this is correct
Azure Synapse Analytics is the correct choice because it is a unified analytics service that natively combines both real-time stream ingestion (via pipelines, Spark Structured Streaming, or Azure Stream Analytics jobs attached to a Synapse workspace) and scalable batch processing, all while enabling serverless SQL or dedicated SQL pools to query files directly in the data lake. This means the retail chain can land streaming POS data into Parquet/Delta files and immediately query those same files with T-SQL, without stitching together separate services. Its integrated architecture is specifically designed to handle exactly this mixed workload pattern.
✗Azure Stream AnalyticsWrong answer — click to see why▾
Why this is wrong here
Azure Stream Analytics is designed for real-time stream processing but lacks native support for combining streaming and batch workloads in a single analytics platform with SQL querying over data lake files. It cannot directly query CSV files in Data Lake Storage Gen2 using SQL without additional services.
★ When this WOULD be the correct answer
If the question required only real-time processing of streaming data from Event Hubs, with output to a sink like Power BI or storage, and no need to combine with batch data or query data lake files directly, Azure Stream Analytics would be the correct choice.
Why candidates choose this
Candidates see 'streaming sales data' and 'Event Hubs' and immediately think of Stream Analytics, overlooking the requirement to combine with batch inventory data and query data lake files directly using SQL.
✗Azure Data Lake AnalyticsWrong answer — click to see why▾
Why this is wrong here
Azure Data Lake Analytics is a batch-only analytics service that does not support real-time streaming ingestion or near real-time dashboards. It also lacks native SQL querying directly on data lake files without creating U-SQL scripts.
★ When this WOULD be the correct answer
A company needs to run complex, custom U-SQL scripts over massive datasets stored in Azure Data Lake Storage, with no requirement for real-time streaming or interactive SQL querying. The focus is on scalable batch processing for data transformation and analytics.
Why candidates choose this
Candidates may confuse Data Lake Analytics with a general-purpose analytics platform because of its name, overlooking its batch-only nature and lack of streaming and SQL-on-lake capabilities.
✗Azure HDInsightWrong answer — click to see why▾
Why this is wrong here
Azure HDInsight is a managed Hadoop/Spark cluster, but it does not provide native SQL querying directly over data in Data Lake Storage Gen2 without additional setup (e.g., Hive or Spark SQL). It also lacks the unified streaming and batch processing capabilities of Synapse Analytics, and requires more manual configuration for near real-time dashboards.
★ When this WOULD be the correct answer
A company needs to run custom big data processing jobs (e.g., using Spark, Hive, or MapReduce) on large datasets in Azure Storage, and requires full control over cluster configuration, libraries, and scaling. The question would specify that the team needs to run custom code (e.g., Python, Scala) for complex transformations, not just SQL queries.
Why candidates choose this
Candidates may associate HDInsight with big data and streaming (via Spark Streaming), and think it can handle both batch and streaming workloads. However, they overlook that Synapse Analytics provides a more integrated, serverless SQL experience directly on the data lake, which is specifically requested in the question.
Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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