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DP-203 Stream Analytics late/out-of-order policies Practice Question
You are designing an Azure Stream Analytics job to process real-time IoT data from thousands of devices. The job must handle late-arriving events (up to 1 hour late) and out-of-order events (up to 5 minutes). Which two temporal policies should you configure?
⚠ Common exam trap
The trap is to think that 'watermark delay' is a configurable policy in Azure Stream Analytics. In ASA, the equivalent is 'late arrival tolerance', not watermark delay. Option C uses Spark terminology and is therefore incorrect.
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
✓
Out of order tolerance window: 5 minutes; Late arrival tolerance window: 1 hour
Azure Stream Analytics uses two temporal policies to handle event timing: the late arrival tolerance window and the out-of-order tolerance window. The late arrival tolerance window defines how long the system waits for events that arrive after their timestamp. The out-of-order tolerance window specifies the maximum time difference allowed for events that arrive out of sequence. In this scenario, you need a late arrival tolerance of 1 hour and an out-of-order tolerance of 5 minutes. Option A directly configures these values correctly. Option C is incorrect because 'watermark delay' is not a configurable temporal policy in Azure Stream Analytics; it is a concept used in Spark Structured Streaming. Therefore, only Option A is correct.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Out of order tolerance window: 5 minutes; Late arrival tolerance window: 1 hour
Why this is correct
Ly sets the out-of-order tolerance window to 5 minutes and the late arrival tolerance window to 1 hour, matching the scenario requirements.
- ✗
Out of order tolerance window: 1 hour; Late arrival tolerance window: 5 minutes
Why it's wrong here
Incorrect. This option reverses the two windows, setting the out-of-order tolerance to 1 hour (too large) and the late arrival tolerance to 5 minutes (too small).
- ✗
Watermark delay: 1 hour; Out of order tolerance: 5 minutes
Why it's wrong here
Incorrect. 'Watermark delay' is not a configurable temporal policy in Azure Stream Analytics. This option uses terminology from Spark Structured Streaming and does not correspond to a valid ASA setting.
- ✗
Use Event Hubs capture to handle late events; no additional configuration needed
Why it's wrong here
Incorrect. Event Hubs capture is used for storing raw events to Azure Blob Storage or Data Lake Store, not for handling late-arriving or out-of-order events in the stream processing logic.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-203 exam frequently reuses these exact scenarios with slightly different constraints.
✓Out of order tolerance window: 5 minutes; Late arrival tolerance window: 1 hourCorrect answer▾
Why this is correct
Ly sets the out-of-order tolerance window to 5 minutes and the late arrival tolerance window to 1 hour, matching the scenario requirements.
✗Out of order tolerance window: 1 hour; Late arrival tolerance window: 5 minutesWrong answer — click to see why▾
Why this is wrong here
This swaps the policies; late arrival should be larger than out-of-order.
✗Watermark delay: 1 hour; Out of order tolerance: 5 minutesWrong answer — click to see why▾
Why this is wrong here
Watermark delay is not directly configurable; it's derived from the two tolerance windows.
✗Use Event Hubs capture to handle late events; no additional configuration neededWrong answer — click to see why▾
Why this is wrong here
Event Hubs capture is for storing raw events, not for handling out-of-order or late arrival in Stream Analytics.
Analysis generated from the official DP-203blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.