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 Data Factory

  Why it's wrong here

  Azure Data Factory is a cloud-based ETL and data integration tool that orchestrates batch pipelines, such as copying data on a recurring schedule or after some conditionally triggered activity. It uses linked services, datasets, and activities that spin up compute on demand, but it lacks native streaming ingestion and event-time processing semantics required for continuous, low-latency analytics. Its strength lies in periodic data movement, not real-time order stream processing.

  When this WOULD be correct

  A company needs to ingest CSV files from Blob Storage daily, transform the data, and load it into a data warehouse. Azure Data Factory would be correct for this scheduled batch ETL workload.

• ✓

  Azure Stream Analytics

  Why this is correct

  Azure Stream Analytics is the only service among the options that is purpose-built for real-time stream processing. It natively ingests data from sources like Azure Event Hubs and IoT Hub, runs continuous SQL-like queries with windowing and reference data joins, and can emit results to sinks such as Power BI or SQL Database with sub-minute latency. For a continuous stream of customer orders, it provides a managed, low-latency pipeline without needing custom code.

• ✗

  Azure Databricks

  Why it's wrong here

  Azure Databricks does offer Spark Structured Streaming, which can process the order stream in micro-batches, but that introduces cluster management overhead, requires writing Scala, Python, or SQL streaming code, and typically incurs higher cost and latency than a dedicated PaaS. For a simple, continuous stream that needs immediate joins or aggregations, Databricks is overkill unless you're also doing advanced machine learning or complex big-data transformation. Its micro-batch model is not tuned for sub-second latency out-of-the-box.

  When this WOULD be correct

  Azure Databricks would be correct if the question required complex data transformations, machine learning model inference on streaming data, or advanced analytics like time-series forecasting on the combined order and inventory data, where Spark's distributed computing and MLlib are needed.

• ✗

  Azure Synapse Pipelines

  Why it's wrong here

  Azure Synapse Pipelines is a data integration and orchestration service, essentially Azure Data Factory's engine inside the Synapse studio, designed for scheduled, batch-oriented workflows. It moves data from numerous sources to destinations, executes script or notebook activities, and triggers on timers or files, but it does not process events in-flight or run continuous streaming queries. Thus it cannot deliver real-time analytics on a live order stream.

  When this WOULD be correct

  Azure Synapse Pipelines would be correct if the question asked for a service to orchestrate nightly batch data movement from Azure Blob Storage to Azure Synapse Analytics for generating sales reports, involving scheduling and monitoring of data pipelines.

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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