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.

• ✗

  Azure Stream Analytics

  Why it's wrong here

  Azure Stream Analytics is purpose-built for continuous, low-latency processing of high-velocity sensor data, using a SQL-like query language inside long-running jobs. It does not provide an interactive T-SQL experience where a user runs ad-hoc queries on demand, and while it can join streaming input with reference data, it is not designed to directly query and join large historical datasets in a data lake. Thus it handles the real-time piece but not the broader analytics requirement.

  When this WOULD be correct

  Azure Stream Analytics would be correct if the question required only real-time anomaly detection on the streaming sensor data without needing to join with historical records or run ad-hoc T-SQL queries. For example, a scenario where you need to filter or aggregate streaming data in real-time and output to a dashboard.

• ✓

  Azure Synapse Analytics

  Why this is correct

  Azure Synapse Analytics is correct because it unifies big data and data warehousing in a single service, letting you run T-SQL queries against both real-time streaming data from Event Hubs and historical batch data in Azure Data Lake Storage. Its serverless SQL pool can query data directly from the lake without loading, and dedicated SQL pools provide familiar relational warehouse semantics. This allows ad-hoc analytics on fresh sensor data joined with historical context, exactly matching the company's requirement.

• ✗

  Azure Data Factory

  Why it's wrong here

  Azure Data Factory is a cloud ETL and data-integration service that orchestrates and monitors pipelines, moving and transforming data between stores such as Event Hubs, Data Lake Storage, and databases. It has no interactive SQL query endpoint, so analysts cannot run ad-hoc T-SQL questions against streaming or historical sensor data directly with it. Its role is preparation and loading, not the analytics query engine needed here.

  When this WOULD be correct

  A question asks: 'Which service should be used to orchestrate the movement and transformation of data from multiple sources into a data warehouse for later analysis?' In that scenario, Azure Data Factory is the correct answer.

• ✗

  Azure HDInsight with Spark

  Why it's wrong here

  Azure HDInsight with Spark can process both streaming and batch data, so it is not wholly incapable; however, it relies on Spark SQL and DataFrame APIs written in Scala, Python, or Java rather than T-SQL, so it does not meet a requirement for T-SQL-based analytics. It also demands ongoing cluster management, configuration, and monitoring, adding operational overhead that a serverless or managed warehouse avoids. For ad-hoc interactive queries, this heavier lift makes it a less direct fit.

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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