PDE Designing Data Processing Systems Practice Question
A data engineer is designing a pipeline that reads from Cloud Pub/Sub, aggregates events into 5-minute windows, and writes the results to BigQuery. The engineer wants to ensure that late-arriving data (up to 2 minutes late) is included in the correct window. Which Dataflow feature should they configure?
⚠ Common exam trap
Many exam-takers confuse window duration adjustments (Option B) or sliding windows (Option A) with the proper late-data handling mechanism, not realizing that allowed lateness and triggers are the correct Dataflow primitives for including late-arriving data in the correct event-time window.
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
✓
Set the allowed lateness to 2 minutes with a trigger that fires on late data
Dataflow's allowed lateness feature (set to 2 minutes) ensures that late-arriving data within that threshold is still assigned to the correct 5-minute window. Combined with a trigger that fires on late data, the pipeline can emit updated results for the window after the watermark passes, which is exactly what the engineer needs to handle late-arriving events up to 2 minutes late.
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 sliding window of 5 minutes with 2-minute slide
Why it's wrong here
A sliding window with a 2-minute slide creates overlapping windows, so each event belongs to multiple aggregates instead of one 5-minute window. It is tempting because sliding windows smooth results over time, and would be correct for moving averages or trend detection, not for discrete 5-minute buckets.
- ✗
Set the window duration to 7 minutes to account for lateness
Why it's wrong here
Lengthening the window to 7 minutes shifts event boundaries, so late events land in the wrong 5-minute aggregate rather than the correct one. It is tempting because a wider window does capture more late data, and would be correct if the requirement were simply to reduce dropped events, not to preserve 5-minute grouping.
- ✓
Set the allowed lateness to 2 minutes with a trigger that fires on late data
Why this is correct
Allowed lateness keeps each 5-minute window's state alive for 2 minutes past the watermark, and a late-data trigger emits updated results when those events arrive. This ensures events up to 2 minutes late land in the correct window rather than being dropped.
- ✗
Use a global window and watermark
Why it's wrong here
A global window has no fixed boundaries, so 5-minute aggregates cannot be produced and late data cannot be assigned to a specific window. It is tempting because global windows with watermarks suit unbounded streams needing no grouping, and would be correct for sessionless whole-stream aggregation rather than fixed 5-minute windows.
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