DP-900 Describe an analytics workload on Azure Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  A: Azure Stream Analytics

  Why it's wrong here

  Azure Stream Analytics is an excellent real-time processing engine that can consume GPS data from Event Hubs and push alerts or outputs to Power BI for live dashboards. However, it cannot perform interactive T-SQL queries on historical data stored in a data lake; its query language is specialized for streaming windows and temporal patterns, not for running arbitrary ad-hoc reports. Since the analysts need to pose T-SQL queries over both current streaming and historical delivery data, Stream Analytics alone is insufficient, and you would need a separate analytical store to serve the dashboard. Thus, it covers only part of the solution, making Synapse the more complete choice.

  When this WOULD be correct

  A question where the primary requirement is real-time analytics on streaming data only, such as detecting anomalies in IoT sensor data, without needing to combine with historical data or run ad-hoc T-SQL queries.

• ✗

  B: Azure Data Lake Analytics

  Why it's wrong here

  Azure Data Lake Analytics is a batch-oriented service that processes data using U-SQL, a language combining SQL syntax with C# expressions, rather than standard T-SQL. Your analysts would need to learn U-SQL and rewrite all their queries, making it unsuitable for ad-hoc T-SQL reporting. It also lacks built-in streaming ingestion, so real-time GPS data would require an extra pipeline to stage data before batch processing, and the service cannot directly serve interactive dashboards to Power BI without intermediate exports. These limitations make it inferior to Synapse for this logistics scenario.

  When this WOULD be correct

  A company needs to run U-SQL queries on massive datasets stored in Azure Data Lake Storage, performing batch transformations and analytics without requiring real-time or interactive T-SQL capabilities.

• ✓

  C: Azure Synapse Analytics

  Why this is correct

  Azure Synapse Analytics is the correct choice because it unifies real-time stream ingestion and historical data analytics in one platform. It provides a SQL pool (dedicated or serverless) that runs standard T-SQL queries against both live streaming data (ingested via Event Hubs) and data lake files like Parquet or Delta, enabling ad-hoc reporting on the combined dataset. Synapse also natively integrates with Power BI, so the delivery-fleet dashboard can be built directly from the same query engine. Hence, it satisfies the requirements for real-time dashboards, T-SQL ad-hoc queries, and historical data access without additional services.

• ✗

  D: Azure Analysis Services

  Why it's wrong here

  Azure Analysis Services is an OLAP engine that builds in-memory tabular models (semantic layers) from pre-existing data; it does not ingest streaming data or query raw data lake files with T-SQL. You would have to transform and load GPS data into a model before analysts could query it, and those queries are restricted to the defined measures and dimensions, not arbitrary ad-hoc T-SQL. While it can power a Power BI dashboard, it cannot provide the flexible, on-the-fly T-SQL exploration requested for both streaming and historical data. Therefore, it does not meet the real-time ingestion and ad-hoc query requirements.

  When this WOULD be correct

  A company needs to create a semantic data model for business users to perform interactive analysis and reporting using tools like Power BI, with data sourced from a pre-built data warehouse. The requirement is for fast, in-memory queries on aggregated data, not raw streaming or ad-hoc T-SQL.

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.

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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