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DP-203 A data engineer at a retail company Practice Question
You are a data engineer at a retail company. You have designed a near real-time data processing solution using Azure Stream Analytics. The input is from Azure Event Hubs, which receives clickstream events from the company's e-commerce website. The output is written to an Azure SQL Database table for reporting. Each event includes fields: UserId, ProductId, EventType (e.g., 'click', 'purchase'), and Timestamp. The requirement is to calculate the number of purchases per product in a 5-minute tumbling window and update a SQL table. The Stream Analytics job has been running for a week, but the reporting team notices that the purchase counts in SQL are consistently lower than expected compared to a direct count from Event Hubs. You suspect that late-arriving events are being dropped. The job's configuration includes a 5-minute tumbling window with no late arrival policy. What should you do to fix the issue without losing data?
⚠ Common exam trap
It's easy for candidates to think increasing the window size or changing the window type (hopping) will fix the issue, but the core problem is the lack of a late arrival policy to handle events that arrive after the window closes.
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
✓
Modify the query to use a larger tumbling window (e.g., 10 minutes) and add a late arrival policy with a 5-minute grace period to allow late events to be included.
The current 5-minute tumbling window has no late arrival policy, so any event that arrives after the window ends is dropped. By increasing the window size to 10 minutes and adding a 5-minute late arrival grace period, you allow events that arrive up to 5 minutes late to still be included in the correct window, matching the actual purchase count from Event Hubs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Modify the query to use a larger tumbling window (e.g., 10 minutes) and add a late arrival policy with a 5-minute grace period to allow late events to be included.
Why this is correct
A larger window with a late arrival policy captures late-arriving events.
- ✗
Modify the query to use TIMESTAMP BY on the EventHubs enqueued time instead of the event's Timestamp field.
Why it's wrong here
Enqueued time reflects ingestion time, not the event time, which could cause incorrect windowing.
- ✗
Change the tumbling window to a hopping window with a 1-minute hop size to increase the frequency of output updates.
Why it's wrong here
Hopping windows still have fixed boundaries and do not handle late arrivals without a late arrival policy.
- ✗
Add a second Stream Analytics job to process late-arriving events separately and union the results.
Why it's wrong here
Adding a second job is complex and may still miss events unless properly coordinated.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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