DP-203 Develop data processing Practice Question
You are building an Azure Stream Analytics job that ingests telemetry from Azure Event Hubs and writes aggregated results to an Azure Synapse Analytics dedicated SQL pool. The job must compute a five-minute tumbling window average per device and tolerate events that arrive up to three minutes late. During testing, you observe that events arriving after the window closes are silently dropped. You need to ensure late events are included in the correct window result. What should you configure in the Stream Analytics job?
⚠ Common exam trap
Many candidates confuse out-of-order tolerance, which reorders events relative to one another, with late arrival tolerance, which keeps a temporal window open for events that arrive after the window boundary.
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
✓
Configure the late arrival tolerance on the temporal window to 00:03:00.
Tumbling windows emit once the window period elapses, and by default any event whose timestamp falls in that period but arrives after emission is discarded. The late arrival tolerance setting explicitly extends the window's acceptance period, so a three-minute value preserves the delayed device telemetry and aggregates it into the correct window. Scaling or retention settings do not affect timestamp-based window membership.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the event ordering policy's Out-of-order events tolerance to 00:03:00.
Why it's wrong here
The out-of-order tolerance controls how long the job waits for events that arrive out of sequence relative to each other, not events that arrive after a window has already emitted. Adjusting this setting will not cause a late event to be folded back into an already-produced tumbling window result, so the dropped events remain dropped in this scenario.
- ✗
Increase the streaming units allocated to the job to at least six.
Why it's wrong here
Streaming units scale the compute capacity and parallelism of the job, which affects throughput and backlog processing, not timestamp semantics. Adding streaming units does not change when a tumbling window closes, so late events whose timestamps belong to an expired window will still be excluded from that window's output.
- ✗
Set the Event Hubs consumer group's message retention to seven days.
Why it's wrong here
Message retention on the event hub controls how long events remain available for replay before they are purged. It does not influence the Stream Analytics windowing logic, so extending retention does not cause a late event to be merged into a tumbling window that has already emitted its result.
- ✓
Configure the late arrival tolerance on the temporal window to 00:03:00.
Why this is correct
Late arrival tolerance extends how long a temporal window such as a tumbling window stays open for events whose timestamp falls inside the window but that physically arrive afterward. Setting it to three minutes lets the job include those delayed telemetry events in the correct five-minute window aggregate instead of discarding them.
Go deeper
Related to this question
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Develop Stream Processing Solutions
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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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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