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.

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  A: Azure Data Factory

  Why this is correct

  Azure Data Factory is purpose-built for hybrid data integration, offering native scheduling via triggers (e.g., daily tumbling windows) for batch ingestion. It uses a Copy Activity that can stage data in Azure Blob Storage and then invoke PolyBase in a separate step, achieving high-throughput parallel loading into dedicated SQL pool. Its incremental copy capability tracks watermarks to copy only changed files, making it the correct orchestration tool for this recurring load scenario.

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  B: Azure Stream Analytics

  Why it's wrong here

  Azure Stream Analytics is a real-time event-processing engine that consumes from sources like Event Hubs, IoT Hub, or Blob Storage only in a streaming context, using temporal windows (hop, tumbling, sliding) for analytics. It has no concept of a scheduled batch job that loads a file snapshot into a dedicated SQL pool; while it can write to Synapse, its output is ephemeral stream results, not a governed daily file ingestion. Its lack of a PolyBase connector for bulk batch loading makes it unsuitable here.

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  C: Azure Databricks

  Why it's wrong here

  Azure Databricks is an Apache Spark-based analytics platform capable of reading from ADLS Gen2 and writing to Synapse via the Spark SQL connector or COPY INTO, but it is not an orchestration service for scheduled bulk loads. Even though it can be automated using Databricks Jobs and a job cluster, it does not natively invoke PolyBase for dedicated SQL pool, and its scheduling requires additional configuration for order-of-operations across activities. Overkill for a simple daily file load, and it requires external orchestration (e.g., ADF) to integrate as part of a pipeline, making it non-ideal.

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  D: Azure Logic Apps

  Why it's wrong here

  Azure Logic Apps is a low-code workflow engine that uses hundreds of connectors for enterprise application integration (e.g., email, CRM, SharePoint), but it moves data by making API calls or copying files at a small scale. It is not optimized for high-throughput data movement into Synapse because it lacks native PolyBase support and enforces action timeouts and throttling limits that would impede large file transfers. For a one-off file copy it might work, but for both scheduled execution and massive parallel loading, it falls short.