The correct choice is a HoppingWindow with a size of 5 minutes and a hop of 1 minute. This configuration computes rolling averages over a five-minute window while emitting results every minute, meeting the requirement for higher-frequency output than the window duration allows. The hop interval controls how often the window slides forward, so a one-minute hop produces overlapping windows that refresh the average at each minute boundary. On the DP-203 exam, this scenario tests your understanding of how HoppingWindow differs from TumblingWindow, which only outputs at the end of the window, and SlidingWindow, which triggers on each event rather than at fixed intervals. A common trap is confusing the hop with the window size—remember that the hop determines output frequency, not the window length. For a memory tip, think of a hopping window as a “staggered snapshot”: the window is the bucket size, the hop is how often you peek inside, so a 5-minute bucket with a 1-minute peek gives you a fresh rolling average every minute.
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of 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. 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 implementing a streaming solution using Azure Stream Analytics. The input is from an IoT Hub receiving telemetry from thousands of devices. The output is to Azure Synapse Analytics dedicated SQL pool. The requirement is to compute rolling averages over a 5-minute tumbling window and write results every minute. Which windowing function and output configuration should you use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a HoppingWindow with size 5 minutes and hop 1 minute.
Option C is correct because a HoppingWindow with a size of 5 minutes and a hop of 1 minute allows you to compute rolling averages over a 5-minute window while producing results every minute. This satisfies the requirement of outputting results at a higher frequency than the window duration, which is not possible with a TumblingWindow (which only outputs at the end of the window) or a SlidingWindow (which outputs on each event, not at fixed intervals).
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.
✗
Use a TumblingWindow with duration of 5 minutes and output every 5 minutes.
Why it's wrong here
Tumbling windows output only at the end of each 5-minute window, not every minute.
✗
Use a SlidingWindow with duration 5 minutes and output every 1 minute.
Why it's wrong here
Sliding windows are event-driven and output for each event, not on a fixed schedule.
✓
Use a HoppingWindow with size 5 minutes and hop 1 minute.
Why this is correct
Hopping windows with a 1-minute hop produce results every minute, each covering the last 5 minutes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a SessionWindow with timeout 5 minutes and maximum duration 10 minutes.
Why it's wrong here
Session windows are based on gaps in data, not suitable for fixed-interval rolling averages.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the output frequency with the window duration, assuming a TumblingWindow can produce results more frequently by adjusting the duration, but only a HoppingWindow with a hop smaller than the size can achieve that.
Trap categories for this question
Command / output trap
Tumbling windows output only at the end of each 5-minute window, not every minute.
Detailed technical explanation
How to think about this question
A HoppingWindow in Azure Stream Analytics is essentially a TumblingWindow that overlaps; the hop size determines how frequently the window advances and results are emitted. Under the hood, each window is computed independently, and overlapping windows can cause duplicate events to be processed multiple times, which is acceptable for rolling averages. In a real-world scenario, this pattern is commonly used for near-real-time dashboards where you need to update metrics like average temperature every minute but base the calculation on the last 5 minutes of data.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a HoppingWindow with size 5 minutes and hop 1 minute. — Option C is correct because a HoppingWindow with a size of 5 minutes and a hop of 1 minute allows you to compute rolling averages over a 5-minute window while producing results every minute. This satisfies the requirement of outputting results at a higher frequency than the window duration, which is not possible with a TumblingWindow (which only outputs at the end of the window) or a SlidingWindow (which outputs on each event, not at fixed intervals).
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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Question Discussion
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