This DP-203 practice question tests your understanding of develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Azure Synapse Spark with Structured Streaming
Why wrong: Spark streaming has higher latency, often >5 seconds.
Azure Stream Analytics
Sub-second latency, native Event Hubs and Cosmos DB connectors.
Azure Data Factory mapping data flows
Why wrong: Data Factory is batch-oriented, not real-time.
Azure Synapse dedicated SQL pool with T-SQL queries
Why wrong: Dedicated SQL pool is for batch analytics, not real-time.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
The trap here is that 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.
Detailed technical explanation
Azure Stream Analytics uses a temporal query language based on T-SQL with extensions for windowing functions (e.g., GROUP BY TumblingWindow, HoppingWindow) and can achieve end-to-end latency as low as 100 milliseconds when configured with 'Streaming' job type and 'Event Serialization' set to JSON. Under the hood, it leverages a distributed stream processing engine that partitions input streams across compute nodes, allowing it to handle 10,000 events per second (20 MB/s) with automatic scaling. A real-world scenario where this matters is a live IoT dashboard monitoring equipment telemetry, where missing the 5-second window could cause delayed alerts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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FAQ
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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