- A
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.
A larger window with a late arrival policy captures late-arriving events.
- B
Modify the query to use TIMESTAMP BY on the EventHubs enqueued time instead of the event's Timestamp field.
Why wrong: Enqueued time reflects ingestion time, not the event time, which could cause incorrect windowing.
- C
Change the tumbling window to a hopping window with a 1-minute hop size to increase the frequency of output updates.
Why wrong: Hopping windows still have fixed boundaries and do not handle late arrivals without a late arrival policy.
- D
Add a second Stream Analytics job to process late-arriving events separately and union the results.
Why wrong: Adding a second job is complex and may still miss events unless properly coordinated.
Quick Answer
The correct approach is to modify the query to use a larger tumbling window, such as 10 minutes, and add a late arrival policy with a 5-minute grace period. This works because Azure Stream Analytics tumbling windows are fixed, non-overlapping time intervals; without a late arrival policy, any event whose timestamp falls within a window but arrives after that window closes is permanently dropped. By increasing the window size to 10 minutes and setting a 5-minute grace period, you effectively allow events that arrive up to 5 minutes late to still be assigned to their correct 5-minute window, matching the true purchase count from Event Hubs. On the DP-203 exam, this scenario tests your understanding of how Stream Analytics handles out-of-order and late-arriving data, a common trap where candidates mistakenly think simply increasing the window size alone will fix the issue—it won’t, because late events still get dropped without an explicit policy. A helpful memory tip: “Window plus grace equals no data waste”—always pair a larger window with a late arrival policy to capture stragglers without losing accuracy.
DP-203 Design and develop data processing Practice Question
This DP-203 practice question tests your understanding of design and develop data processing. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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 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?
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.
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a tumbling window that closes at the end of the window duration. Without a late arrival policy, events with a timestamp older than the window end are discarded. The late arrival policy (specified via the LATE ARRIVAL clause) defines a grace period during which events can still be assigned to the correct window, even if they arrive after the window closes. This is critical for near real-time scenarios where network delays or client-side buffering cause events to arrive out of order.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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
Got this wrong? Here's your next step.
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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. — Option A is correct because 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.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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