DP-203 Develop data processing Practice Question
You are designing a data processing pipeline in Azure Synapse Analytics that ingests streaming data from Azure Event Hubs and stores it in a dedicated SQL pool. The data must be available for querying within 5 minutes of ingestion. Which processing approach should you recommend?
⚠ Common exam trap
Candidates often confuse batch-oriented tools (Data Factory, PolyBase) or general-purpose streaming frameworks (Spark Structured Streaming) with the dedicated, low-latency stream processing service (Stream Analytics) that is optimized for sub-minute latency to Synapse SQL pools.
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
✓
Use Azure Stream Analytics with a dedicated SQL pool output and configure a 1-minute window.
Azure Stream Analytics is purpose-built for real-time stream processing and can output directly to a dedicated SQL pool. By configuring a 1-minute window, you ensure data is materialized in the SQL pool well within the 5-minute SLA, meeting the latency requirement with headroom.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Azure Data Factory with a tumbling window trigger set to 5 minutes.
Why it's wrong here
Data Factory is batch-oriented and the trigger adds overhead; it cannot match the latency of a streaming solution.
- ✓
Use Azure Stream Analytics with a dedicated SQL pool output and configure a 1-minute window.
Why this is correct
Stream Analytics provides sub-minute latency and is designed for real-time ingestion into Synapse dedicated SQL pool.
- ✗
Use PolyBase to load data from Event Hubs into the dedicated SQL pool every 5 minutes.
Why it's wrong here
PolyBase is for batch loading from external sources, not for streaming ingestion.
- ✗
Use Spark Structured Streaming in Azure Synapse to write micro-batches every 5 minutes.
Why it's wrong here
Spark Structured Streaming can achieve low latency but often has higher overhead; micro-batches every 5 minutes may not guarantee <5 minutes end-to-end.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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.
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
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