Question 32 of 760
DP-203 Develop data processing Practice Question
You are designing a data processing solution in Azure Synapse Analytics. The solution must process streaming data from Azure Event Hubs and store the results in a dedicated SQL pool. You need to choose the most appropriate service for near real-time ingestion with minimal latency. What should you use?
⚠ Common exam trap
Many candidates confuse 'near real-time' with 'batch processing' and choose Azure Data Factory (option C) because it is a familiar data integration tool, overlooking that it lacks native streaming capabilities and introduces latency from scheduled pipeline runs.
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 the correct choice because it is purpose-built for near real-time stream processing with sub-second latency, directly integrates with Azure Event Hubs as an input source and dedicated SQL pool as an output sink, and provides a SQL-like query language for defining transformations. This minimizes architectural complexity and latency compared to other services.
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 Databricks with Structured Streaming
Why it's wrong here
While capable, it introduces additional complexity and latency compared to Stream Analytics for this use case.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is optimized for low-latency streaming and can directly ingest from Event Hubs and write to SQL pools.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is primarily for batch data integration, not near real-time streaming.
- ✗
Azure Functions with Event Hub trigger
Why it's wrong here
Azure Functions are not designed for high-throughput streaming scenarios.
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Last reviewed: Jun 24, 2026
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