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
Spark Structured Streaming on HDInsight processes micro-batches, so end-to-end latency typically lands in seconds rather than sub-second, and cluster provisioning adds operational overhead. It suits large-scale batch or complex stateful transformations, not the low-latency requirement here. Azure Stream Analytics delivers sub-second processing for this pattern.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics provides fully managed, sub-second stream processing with built-in windowing and direct output to Data Lake Storage Gen2. Its low-latency engine handles 10,000 events per second while archiving to ADLS Gen2, meeting both latency and storage requirements.
- ✗
Azure Data Factory
Why it's wrong here
Data Factory pipelines run on scheduled or event-triggered orchestration cycles, so latency is measured in minutes, not sub-second. It is the right tool for batch movement and orchestration between stores, but it cannot satisfy continuous low-latency stream processing of 10,000 events per second.
- ✗
Azure Event Hubs
Why it's wrong here
Event Hubs is an ingestion and buffering service; it stores and delivers the stream but performs no transformation or query logic. The stem asks which service processes the stream, so Event Hubs would be correct as the ingestion layer feeding a processing engine, not as the processor itself.
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