DP-203 Develop data processing Practice Question
You are processing streaming data from IoT devices using Azure Stream Analytics. The data includes temperature readings and device IDs. You need to calculate the average temperature per device over a 5-minute window, sliding every 1 minute. Which window function should you use?
⚠ Common exam trap
Candidates often confuse 'sliding' with 'hopping' — a Sliding window in Stream Analytics is event-driven and does not produce periodic outputs, whereas a Hop window is time-driven and explicitly supports overlapping fixed-size windows with a hop interval.
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
✓
Hop window
A Hop window in Azure Stream Analytics allows you to specify a window size (5 minutes) and a hop size (1 minute), creating overlapping windows that slide forward every minute. This matches the requirement to calculate the average temperature per device over a 5-minute period, recalculated every minute, as the hop window outputs results at each hop interval while retaining data across overlapping windows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Hop window
Why this is correct
A hopping window advances by a fixed hop interval while spanning a longer window size, so a five-minute window sliding every minute is expressed as Hop(5m, 1m). Tumbling windows cannot overlap, and sliding windows in Stream Analytics are triggered by events rather than clock time, so neither meets the per-device one-minute cadence.
- ✗
Session window
Why it's wrong here
Session windows group events separated by gaps of inactivity, so their start and end depend on the data rather than fixed clock intervals, producing neither the 5-minute length nor the 1-minute slide. It is tempting because it suits activity bursts, such as user sessions, where grouping by idle gaps is the correct approach.
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Sliding window
Why it's wrong here
Sliding windows overlap, so a 5-minute window sliding every 1 minute would recompute overlapping aggregates, not the single non-overlapping 5-minute average per device required. Tumbling windows are correct here. Sliding windows suit scenarios needing continuously updated rolling averages, such as detecting sustained temperature spikes across overlapping intervals.
- ✗
Tumbling window
Why it's wrong here
Tumbling windows are fixed-length, non-overlapping and contiguous, so a 5-minute window cannot advance every 1 minute. It is tempting because it is the standard choice for simple periodic aggregation, and it would be correct if the requirement were non-overlapping 5-minute averages with no sliding interval.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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