PDE Designing Data Processing Systems Practice Question
You are designing a Dataflow pipeline that reads from Pub/Sub and writes to BigQuery. The pipeline must handle late-arriving data (up to 1 hour) and group events into 10-minute windows. Which configuration is correct?
⚠ Common exam trap
PDE often tests whether candidates conflate windowing (grouping) with triggering (emission timing) — the trap is choosing a trigger-only answer (global windows with a periodic trigger) when the requirement is per-window aggregation with late 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
✓
Use fixed windows of 10 minutes with allowed lateness of 1 hour and a trigger that fires after watermark plus early firings
Fixed windows of 10 minutes match the required grouping interval, allowed lateness of 1 hour accommodates late-arriving data up to one hour, and a trigger that fires after the watermark plus early firings ensures results are emitted promptly while still accepting late data. This combination is the canonical Apache Beam pattern for windowed aggregation with late data on a streaming pipeline.
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 global windows with a trigger that fires every 10 minutes
Why it's wrong here
Global windows place every element in one unbounded window, so a 10-minute trigger emits accumulated panes rather than grouping events into discrete 10-minute windows as required. Triggers control when results fire, not how elements are assigned. Fixed windows with allowed lateness of one hour would satisfy both the grouping and late-data requirements.
- ✗
Use sliding windows of 10 minutes with a 5-minute period and allowed lateness of 1 hour
Why it's wrong here
Sliding windows emit overlapping results every five minutes, so each event lands in multiple 10-minute windows and downstream aggregation double-counts. Fixed windows are required to group events into discrete 10-minute buckets. Sliding windows suit rolling averages or continuous trend detection, where overlapping intervals are the intended output.
- ✗
Use fixed windows of 10 minutes with allowed lateness of 0 seconds
Why it's wrong here
Allowed lateness of 0 seconds discards any event arriving after the 10-minute window closes, so the required one-hour late data is dropped. It is tempting because fixed windows with zero lateness suit strictly in-order, real-time streams where no backlog is expected.
- ✓
Use fixed windows of 10 minutes with allowed lateness of 1 hour and a trigger that fires after watermark plus early firings
Why this is correct
Fixed 10-minute windows satisfy the grouping requirement, while one hour of allowed lateness lets late events still update their window's results. The trigger firing on watermark plus early firings emits speculative results promptly, then corrects them once the watermark passes, so no late data within the hour is dropped.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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