DP-203 Azure Stream Analytics Practice Question
Which TWO Azure services can be used to perform real-time data processing on streaming data?
⚠ Common exam trap
Candidates often assume that any service capable of ingesting streaming data (like Synapse dedicated SQL pool) qualifies as real-time processing, or they overlook Databricks because it is often associated with batch analytics. The trap is that Synapse's dedicated SQL pool is a data warehouse, not a streaming engine; Databricks' Structured Streaming is a powerful real-time processing engine on Azure.
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 (B) is purpose-built for real-time stream processing: it ingests continuous data from sources such as Azure Event Hubs, IoT Hub, or Blob Storage and runs SQL-like queries with temporal windows to produce low-latency outputs. Azure Databricks (E) also supports real-time processing because its Structured Streaming engine (built on Apache Spark) can continuously process streaming data from Event Hubs, Kafka, or IoT Hub with exactly-once semantics. Azure Synapse Analytics dedicated SQL pool (A) is a batch-oriented MPP data warehouse, not a real-time streaming engine, so it does not fit. Azure Data Factory (C) is an orchestration/ETL service for scheduled batch data movement and transformation, not continuous stream processing. Azure Logic Apps (D) is a workflow automation and integration service triggered by events, but it does not perform real-time analytical stream processing.
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 (dedicated SQL pool)
Why it's wrong here
Azure Synapse Analytics dedicated SQL pool is a data warehouse optimized for batch and interactive queries; it cannot perform real-time stream processing. It can ingest streaming data via copy commands but does not execute continuous queries on the stream.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics runs continuous SQL-like queries over event streams from Event Hubs or IoT Hub, emitting results with sub-second latency. This satisfies the real-time processing requirement, since batch engines and storage services cannot transform unbounded data as it arrives.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory orchestrates batch and scheduled data movement through pipelines, connectors and triggers; it cannot process an unbounded stream continuously. It is tempting because it does move and transform data, and it would be the right pick for scheduled batch ingestion or ELT workloads rather than real-time stream processing.
- ✗
Azure Logic Apps
Why it's wrong here
Logic Apps orchestrates workflows and connectors on triggers, offering no stream processing operators such as windowing or event-time handling. It would be correct for integrating SaaS systems and automating business processes, not for continuous computation over streaming data.
- ✓
Azure Databricks
Why this is correct
Azure Databricks provides Structured Streaming on Apache Spark, letting engineers process continuous event streams with the same codebase used for batch. This satisfies the real-time requirement, unlike storage or orchestration services that cannot transform unbounded data as it arrives.
Go deeper
Related to this question
Learn chapter
Orchestrate Data Movement and Transformation
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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