- A
Out of order tolerance window: 5 minutes; Late arrival tolerance window: 1 hour
Correct. This option correctly sets the out-of-order tolerance window to 5 minutes and the late arrival tolerance window to 1 hour, matching the scenario requirements.
- B
Out of order tolerance window: 1 hour; Late arrival tolerance window: 5 minutes
Why wrong: 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).
- C
Watermark delay: 1 hour; Out of order tolerance: 5 minutes
Why wrong: 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.
- D
Use Event Hubs capture to handle late events; no additional configuration needed
Why wrong: 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.
DP-203 Stream Analytics late/out-of-order policies Practice Question
This DP-203 practice question tests your understanding of design and develop data processing. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. A key principle to apply: late arrival tolerance window. 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 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?
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.
Key principle: Late arrival tolerance window
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
Correct. This option correctly sets the out-of-order tolerance window to 5 minutes and the late arrival tolerance window to 1 hour, matching the scenario requirements.
Related concept
Late arrival tolerance window
- ✗
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
Correct. This option correctly 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?”
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, Azure Stream Analytics implements these policies by adjusting the watermark (the internal timestamp up to which events are considered complete). The late arrival tolerance window effectively delays the watermark by the specified duration, allowing late events to be included in windowed aggregations. The out-of-order tolerance window reorders events within that time span before processing, ensuring correct temporal alignment. In a real-world IoT scenario with devices in different time zones or with network latency, misconfiguring these windows can lead to either dropped events or inflated latency in output.
KKey Concepts to Remember
- Late arrival tolerance window
- Out-of-order tolerance window
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Late arrival tolerance window
Real-world example
How this comes up in practice
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. Late arrival tolerance window Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this DP-203 question test?
Design and develop data processing — This question tests Design and develop data processing — Late arrival tolerance window.
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DP-203 question wrong?
Review late arrival tolerance window, then practise related DP-203 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Late arrival tolerance window
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Last reviewed: Jun 11, 2026
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