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

  Stream Analytics is purpose-built for near-real-time event processing, allowing you to write continuous SQL queries over data as it streams from sources like Event Hubs. However, it does not provide the ability to interactively query large stored datasets or to combine real-time and historical data without writing results to a separate destination. As a result, it cannot serve as the single unified platform needed for both streaming and batch analytics, leaving a gap in the solution.

  When this WOULD be correct

  An exam question that requires only real-time analytics on streaming data (e.g., IoT telemetry) with simple aggregations and no need for historical batch joins or unified batch/stream processing.

• ✗

  Azure Data Factory

  Why it's wrong here

  Azure Data Factory is a cloud-based ETL and data orchestration service that automates the movement and transformation of data between supported sources and destinations. It does not provide a compute layer capable of serving interactive BI dashboards or running complex analytical queries, nor does it ingest and process unbounded streaming events in real time. While you could use it to build pipelines that feed data into analytics engines, on its own it is not an analytics platform that meets the end-to-end requirements.

  When this WOULD be correct

  A company needs to orchestrate and automate data movement from multiple on-premises and cloud sources to a central data lake, performing scheduled transformations without real-time requirements.

• ✓

  Azure Synapse Analytics

  Why this is correct

  Synapse Analytics provides a unified analytics experience with support for both real-time stream processing (via Synapse Pipelines and Spark structured streaming) and large-scale batch analytics using dedicated SQL pools or serverless SQL. It meets all requirements.

• ✗

  Azure HDInsight

  Why it's wrong here

  Azure HDInsight offers managed Apache Hadoop and Spark clusters that can handle both batch and streaming workloads, but it requires ongoing cluster lifecycle management, including configuring autoscaling, securing connectivity, and monitoring session health. In contrast to Azure Synapse Analytics, HDInsight lacks a built-in serverless SQL engine, a unified workspace, and integrated data governance, so you must stitch together many separate components manually. This extra operational complexity makes it a less seamless choice for a comprehensive analytics solution.

  When this WOULD be correct

  A question where the requirement is to process large-scale data using open-source frameworks like Hadoop, Spark, or Hive, with full control over cluster configuration and scaling, and where a unified stream/batch experience is not required.

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?”