DP-900 Describe core data concepts 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 Synapse Analytics

  Why it's wrong here

  Azure Synapse Analytics is an enterprise-grade distributed analytics service designed for large-scale data warehousing and big data workloads, but it is not a lightweight streaming engine. While Synapse can ingest streaming data via Event Hubs into its SQL or Spark pools, that ingestion pattern is effectively micro-batch landing, not continuous event-at-a-time processing. Achieving near real-time IoT processing in Synapse would require building custom orchestration around Event Hubs and incur additional latency that a dedicated streaming service avoids. Thus, it is overkill for simple IoT telemetry processing and lacks the native temporal-window SQL constructs that make stream processing straightforward.

• ✗

  Azure Databricks

  Why it's wrong here

  Azure Databricks, while supporting stream processing through Apache Spark Structured Streaming, is not ideal for near real-time processing of IoT device streams due to its micro-batch architecture, which typically introduces higher latency than dedicated real-time stream processing services. It is tempting because Databricks is a powerful analytics platform, excellent for complex data transformations, machine learning, and advanced analytics on large datasets, including scenarios where micro-batch processing of streaming data is acceptable for less stringent latency requirements.

• ✓

  Azure Stream Analytics

  Why this is correct

  Azure Stream Analytics is a fully managed, purpose-built stream-processing service that handles near real-time IoT telemetry with low latency. It provides a SQL-like query language that natively supports temporal windows, sliding windows, and event-time processing, allowing filters, aggregations, and even anomaly detection directly on the stream. Crucially, it has a native Cosmos DB sink and built-in connectors to Event Hubs, IoT Hub, and other Azure services, eliminating the need for custom glue code. Because it processes each event as it arrives rather than in micro-batches, it is the ideal choice for real-time IoT scenarios that require prompt alerts or continuous output.

• ✗

  Azure Data Factory

  Why it's wrong here

  Azure Data Factory is a cloud data integration service focused on batch ETL/ELT, pipeline orchestration, and scheduled data movement. It does not provide continuous, event-at-a-time stream processing; rather, pipelines are triggered on schedules or discrete events such as a blob being created, and each run processes a finite set of data. While ADF can move batches of data from Event Hubs to a store, it lacks stream-processing primitives like watermarking, windowed aggregates, and sliding time windows that are essential for per-event IoT analysis. Attempting to force near real-time streaming through ADF would require frequent pipeline runs, adding latency and operational complexity without true streaming semantics.

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