Develop data processing →mediumMultiple Choice

DP-203 Develop data processing Practice Question

You are implementing a streaming pipeline in Azure Stream Analytics that reads from an Azure Event Hub and writes aggregated results to an Azure Synapse Analytics dedicated SQL pool. The query groups events into 30-second windows. You need to ensure that the job can handle late-arriving events up to 2 minutes after the window closes without dropping them. What should you configure?

⚠ Common exam trap

Candidates often confuse out-of-order tolerance with late arrival tolerance, which control different aspects of event timing.

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 event ordering policy's late arrival tolerance to 2 minutes in the job's Event Ordering settings.

Late-arriving events are handled by the late arrival tolerance in the event ordering policy. This setting defines how long Stream Analytics waits for events that arrive after the window end before finalizing results. Configuring it to 2 minutes ensures events up to 2 minutes late are included. Other options affect performance or window semantics but not late-event inclusion.

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 late arrival tolerance to 2 minutes in the job's Event Ordering settings.
Why this is correct
The late arrival tolerance in the event ordering policy tells Stream Analytics how long to wait for out-of-order events before finalizing a window. Setting it to 2 minutes ensures events arriving up to 2 minutes late are included in the correct window. This directly addresses the requirement without changing the query or output.
✗
Change the window type from Tumbling to Sliding with a 2-minute duration.
Why it's wrong here
Switching to a Sliding window changes how windows overlap and are computed, but it does not control late-arrival tolerance. A Sliding window of 2 minutes would produce overlapping aggregates, not solve the late-event inclusion problem. The late arrival tolerance setting is the correct control for this scenario.
✗
Enable the 'Adjust event ordering' option and set the out-of-order tolerance to 2 minutes.
Why it's wrong here
Out-of-order tolerance handles events that arrive out of sequence but still within the window, not events that arrive after the window has closed. Late-arriving events after window closure are governed by late arrival tolerance, not out-of-order tolerance. Setting only out-of-order tolerance would not meet the requirement.
✗
Increase the streaming units of the Stream Analytics job to accommodate the late events.
Why it's wrong here
Streaming units scale compute resources for throughput and parallelism, but they do not affect how late-arriving events are handled relative to window boundaries. Increasing SUs may improve performance but will not prevent late events from being dropped or assigned to the wrong window based on the default tolerance.

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DP-203 Develop data processing Practice Question

Why each option matters

Option-by-option breakdown

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