The correct answer is SELECT DeviceType, COUNT(*) FROM Input GROUP BY DeviceType, TumblingWindow(second, 10). This query works because a tumbling window is a series of fixed-size, non-overlapping and contiguous time intervals, making it ideal for counting events per device type every 10 seconds without any overlap or gaps. The GROUP BY clause ensures each device type is counted separately within each window, which directly satisfies the requirement to count events per device type every 10 seconds from Event Hubs. On the DP-203 exam, this tests your understanding of windowing functions in Azure Stream Analytics, where tumbling windows are often contrasted with hopping or sliding windows. A common trap is choosing a hopping window with a smaller hop size, which would produce overlapping counts. Remember the memory tip: “Tumbling windows are like bricks—they stack neatly without overlapping,” so when you need a clean, periodic count per interval, always reach for TumblingWindow.
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 designing a streaming job in Azure Stream Analytics. The job needs to count the number of events per device type every 10 seconds. The input is from Event Hubs. Which query 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
✓
SELECT DeviceType, COUNT(*) FROM Input GROUP BY DeviceType, TumblingWindow(second, 10)
Option B is correct because a TumblingWindow(second, 10) produces non-overlapping, fixed-size 10-second windows, which is exactly what is needed to count events per device type every 10 seconds. The GROUP BY clause groups by DeviceType and the window, ensuring each device type gets its own count per window. This query meets the requirement without overlapping or sliding behavior.
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.
✗
SELECT DeviceType, COUNT(*) FROM Input GROUP BY DeviceType, SessionWindow(second, 10, 30)
Why it's wrong here
Session window is based on inactivity, not fixed time.
✓
SELECT DeviceType, COUNT(*) FROM Input GROUP BY DeviceType, TumblingWindow(second, 10)
Why this is correct
Tumbling window outputs exactly every 10 seconds.
Related concept
Read the scenario before looking for a memorised answer.
✗
SELECT DeviceType, COUNT(*) FROM Input GROUP BY DeviceType, HoppingWindow(second, 10, 1)
Why it's wrong here
Hopping window with hop 1 produces results every second, not every 10 seconds.
✗
SELECT DeviceType, COUNT(*) FROM Input GROUP BY DeviceType, SlidingWindow(second, 10)
Why it's wrong here
Sliding window outputs for each event, not on a fixed interval.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse HoppingWindow with TumblingWindow, thinking a hop size of 1 second still produces 10-second intervals, but HoppingWindow emits results at every hop, not at the window duration, leading to incorrect output frequency.
Trap categories for this question
Command / output trap
Sliding window outputs for each event, not on a fixed interval.
Detailed technical explanation
How to think about this question
Tumbling windows in Azure Stream Analytics are aligned to the window start time (e.g., epoch) and do not overlap, making them ideal for periodic aggregations like per-minute or per-10-second counts. Under the hood, the job uses checkpointing and watermarking to handle late-arriving events, but tumbling windows guarantee exactly one output per window per group key. In real-world scenarios, this pattern is common for dashboards showing device telemetry counts at fixed intervals, where overlapping or session windows would distort the metric.
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.
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: SELECT DeviceType, COUNT(*) FROM Input GROUP BY DeviceType, TumblingWindow(second, 10) — Option B is correct because a TumblingWindow(second, 10) produces non-overlapping, fixed-size 10-second windows, which is exactly what is needed to count events per device type every 10 seconds. The GROUP BY clause groups by DeviceType and the window, ensuring each device type gets its own count per window. This query meets the requirement without overlapping or sliding behavior.
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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