DP-203 Develop data processing Practice Question
You are building an Azure Stream Analytics job that reads JSON telemetry from an Azure Event Hub, calculates a 5-minute tumbling window average per device, and writes results to an Azure Synapse Analytics dedicated SQL pool. The stream must handle occasional bursts of late-arriving events by including events that arrive up to 3 minutes after the window closes. You need to configure the job's event ordering settings to meet the late-arrival requirement while minimizing memory usage. What should you do?
⚠ Common exam trap
Many exam-takers confuse late arrival tolerance with out-of-order tolerance, assuming both control late events when only late arrival tolerance extends the window past its end time.
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
✓
Set the late arrival tolerance to 3 minutes and the out-of-order tolerance to 0 seconds.
Late-arriving events are handled by the late arrival tolerance, which delays window finalization to include events that arrive after the window end. Out-of-order tolerance is separate and controls reordering within the stream. To meet the 3-minute late-arrival requirement while minimizing memory, set late arrival tolerance to 3 minutes and keep out-of-order tolerance low, as only late arrivals need extended buffering.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the streaming units for the job and leave the default event ordering settings unchanged.
Why it's wrong here
Streaming units scale compute resources but do not change event ordering behavior. Default late arrival tolerance is 0 seconds, so events arriving up to 3 minutes late would still be dropped. Adding streaming units would not solve the late-arrival requirement and would increase cost without addressing the core issue of window finalization timing.
- ✓
Set the late arrival tolerance to 3 minutes and the out-of-order tolerance to 0 seconds.
Why this is correct
Late arrival tolerance defines how long Stream Analytics waits for events that arrive after the window end before finalizing window results. Setting it to 3 minutes directly satisfies the requirement. Out-of-order tolerance controls reordering of events within the stream, not late arrivals, so setting it to 0 seconds minimizes buffering and memory, which is appropriate here because the scenario only requires handling late-arriving events.
- ✗
Set the out-of-order tolerance to 3 minutes and the late arrival tolerance to 0 seconds.
Why it's wrong here
Out-of-order tolerance adjusts the timeline for events that arrive out of sequence relative to their timestamps, but it does not extend the window past its end time for late-arriving events. With late arrival tolerance at 0 seconds, events arriving after the window closes would be dropped, failing the requirement. This configuration would also increase memory usage by buffering events for reordering, which is not needed.
- ✗
Set both the late arrival tolerance and the out-of-order tolerance to 3 minutes.
Why it's wrong here
Setting both tolerances to 3 minutes would handle late arrivals, but unnecessarily increasing out-of-order tolerance forces Stream Analytics to buffer more events for reordering, consuming additional memory. The requirement explicitly asks to minimize memory usage, so the out-of-order tolerance should remain low. Only the late arrival tolerance needs to be extended to 3 minutes.
Go deeper
Related to this question
Learn chapter
Implement Azure Stream Analytics
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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JA
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
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.