DP-203 Develop data processing Practice Question
You are designing a data processing pipeline in Azure Synapse Analytics that reads streaming data from Azure Event Hubs, performs aggregations in real time, and writes results to Azure Cosmos DB for a dashboard. The data volume is 10,000 events per second with 2 KB each. The latency requirement is under 5 seconds from event ingestion to dashboard visibility. Which technology should you use for the real-time aggregation?
⚠ Common exam trap
Watch out — candidates often confuse Azure Synapse Spark Structured Streaming (which is micro-batch, not true streaming) with a real-time engine, or they assume Azure Data Factory can handle streaming data because it supports 'real-time' triggers, but it cannot perform in-flight aggregations with sub-second latency.
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 a fully managed, real-time analytics service designed specifically for low-latency stream processing. It can ingest data from Azure Event Hubs, perform windowed aggregations (e.g., tumbling, hopping, sliding windows) with sub-second latency, and output directly to Azure Cosmos DB, meeting the 5-second latency requirement for the dashboard.
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 Spark with Structured Streaming
Why it's wrong here
Spark streaming has higher latency, often >5 seconds.
- ✓
Azure Stream Analytics
Why this is correct
Sub-second latency, native Event Hubs and Cosmos DB connectors.
- ✗
Azure Data Factory mapping data flows
Why it's wrong here
Data Factory is batch-oriented, not real-time.
- ✗
Azure Synapse dedicated SQL pool with T-SQL queries
Why it's wrong here
Dedicated SQL pool is for batch analytics, not real-time.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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.
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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Senior Network & Security Engineer · founder of Courseiva
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