DP-203 Develop data processing Practice Question
You are designing a streaming data solution for IoT devices that generate 10,000 events per second. The data must be processed with sub-second latency and then stored in Azure Data Lake Storage Gen2 for archival. Which Azure service should you use for the stream processing?
⚠ Common exam trap
Candidates often confuse Azure Event Hubs (ingestion) with Azure Stream Analytics (processing), or they overcomplicate the solution by choosing HDInsight Spark when a simpler, fully managed service meets the sub-second latency requirement.
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 real-time stream processing with sub-second latency, and it natively integrates with Azure Data Lake Storage Gen2 for output. It can handle 10,000 events per second using its streaming unit scaling, and its SQL-like query language allows for low-latency transformations without the overhead of cluster management.
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 HDInsight including Spark Structured Streaming
Why it's wrong here
HDInsight Spark Structured Streaming has higher latency and is more suited for micro-batch processing, not sub-second latency.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics provides sub-second latency stream processing and native output to Azure Data Lake Storage Gen2.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is an orchestration and ETL tool, not a real-time stream processing engine.
- ✗
Azure Event Hubs
Why it's wrong here
Azure Event Hubs is a data ingestion service, not a processing engine.
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