PDE Ingesting and Processing the Data Practice Question
A media company streams user interaction events into Pub/Sub and processes them with a Dataflow streaming pipeline that writes to BigQuery. During peak hours, the pipeline's watermark lags significantly behind real time, and late-arriving events are being dropped. The team wants late events to be included in windowed aggregations for up to 30 minutes after the window closes. Which Dataflow configuration should they apply?
⚠ Common exam trap
It's easy for candidates to confuse watermark lag with allowed lateness; scaling workers addresses throughput, not the windowing rule that discards late elements.
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 on the windowing transform to 30 minutes and update the aggregation to emit late panes.
Allowed lateness extends the period during which a window accepts late data after the watermark passes the window end. Setting it to 30 minutes and emitting late panes ensures events arriving within that window update the aggregation instead of being dropped, which is the correct way to handle late-arriving streaming data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch the pipeline to use processing-time windows instead of event-time windows.
Why it's wrong here
Processing-time windows use the system clock when elements arrive, so they do not drop events based on event timestamps. However, they produce non-deterministic, non-reproducible results and do not preserve event-time semantics, so they are not an appropriate fix when the goal is to include late events in event-time aggregations.
- ✗
Increase the number of worker threads and enable autoscaling to reduce the watermark lag.
Why it's wrong here
Adding workers and enabling autoscaling can improve throughput and reduce processing delay, but it does not change the windowing semantics. Elements that arrive after the watermark passes the window end are still considered late and dropped unless allowed lateness is configured, so this does not guarantee inclusion of events arriving up to 30 minutes late.
- ✓
Set the allowed lateness on the windowing transform to 30 minutes and update the aggregation to emit late panes.
Why this is correct
Allowed lateness controls how long after a window closes Dataflow continues to accept and process late-arriving elements for that window. Setting it to 30 minutes and ensuring the aggregation emits late panes lets events arriving within that period update results, which directly addresses the dropped late events while keeping the watermark behavior unchanged.
- ✗
Configure the Pub/Sub subscription to retain messages for 30 minutes and replay them.
Why it's wrong here
Subscription retention and replay can redeliver messages that were not acknowledged, but the pipeline already received and processed these events; the issue is that the windowing logic discarded them as late. Replay would duplicate already-processed events and still not change the window's allowed lateness, so it does not solve the stated problem.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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