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DP-203 Develop data processing Practice Question

A company is ingesting streaming data from IoT devices into Azure Event Hubs. The data must be processed in near real-time and stored in Azure Synapse Analytics for reporting. The solution must handle late-arriving data and ensure exactly-once semantics. Which Azure service should you use for stream processing?

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 Stream Analytics

(Azure Stream Analytics) is correct because it supports exactly-once semantics for output to Azure Synapse Analytics, handles late-arriving data via adjustable event ordering policies, and is purpose-built for real-time stream processing. Option A (Azure Data Factory) is incorrect as it is primarily a batch ETL service and not designed for near-real-time streaming. Option B (Azure Synapse Spark Structured Streaming) can process streaming data but does not provide exactly-once semantics to Synapse out of the box without additional configuration. Option D (Azure Event Hubs with Capture) is incorrect because it only captures raw event data to storage and does not perform stream processing or guarantee exactly-once delivery to Synapse.

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 Data Factory with Event Hubs source

    Why it's wrong here

    Data Factory is batch-oriented.

  • Azure Synapse Spark Structured Streaming

    Why it's wrong here

    Complex to achieve exactly-once; checkpointing required.

  • Azure Stream Analytics

    Why this is correct

    Provides exactly-once delivery and can handle late arrivals.

  • Azure Event Hubs with Capture

    Why it's wrong here

    Capture is for storage, not processing.

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