PDE Designing Data Processing Systems Practice Question
You need to analyse streaming data from thousands of IoT devices, each sending temperature readings every second. You want to calculate the average temperature per device over the last 5 minutes, updating every minute. Which windowing strategy should you use in Dataflow?
⚠ Common exam trap
PDE often tests the confusion between sliding and fixed windows — candidates must recognize that 'updating every minute over the last 5 minutes' requires overlapping windows (sliding), not non-overlapping fixed windows or global windows with triggers.
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
✓
Sliding windows of length 5 minutes with a period of 1 minute
Sliding windows of length 5 minutes with a period of 1 minute produce a new window every minute, each covering the previous 5 minutes of data — exactly matching the requirement to compute a 5-minute average that updates every minute. This is the canonical use case for sliding windows in Dataflow/Beam.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Sliding windows of length 5 minutes with a period of 1 minute
Why this is correct
Sliding windows of five minutes with a one-minute period recompute overlapping five-minute averages every minute, so each device's rolling average refreshes continuously. This matches the required five-minute lookback and one-minute update cadence, which fixed or session windows cannot provide.
- ✗
Global windows with a trigger firing every minute
Why it's wrong here
Global windows place all elements in one unbounded window, so a one-minute trigger emits an average over the entire stream history, not the last five minutes. It is tempting because global windows with triggers suit unbounded streams, and would be correct when the aggregate genuinely spans all data received.
- ✗
Fixed windows of 5 minutes
Why it's wrong here
Fixed five-minute windows align to epoch boundaries, so a reading arriving mid-window is averaged only with the remainder of that window, not the trailing five minutes. It is tempting because fixed windows suit periodic reporting, and would be correct if averages were required per calendar-aligned interval rather than continuously.
- ✗
Session windows with a gap duration of 1 minute
Why it's wrong here
Session windows group by activity gaps, so a one-minute gap splits a device's stream into separate sessions rather than maintaining a continuous trailing five-minute average. It is tempting because session windows suit bursty, user-driven event streams, and would be correct when analysis should follow periods of activity separated by inactivity.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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