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PDE Practice Question: A team runs a Dataflow streaming pipeline that…
A team runs a Dataflow streaming pipeline that reads from Pub/Sub, windows events by processing time, and writes to BigQuery. Some late-arriving events are being dropped. The requirement is to include all events that arrive within 10 minutes of the watermark. Which pipeline configuration should be used?
⚠ Common exam trap
Google Cloud often tests the distinction between processing time and event time, and the exact value of allowed lateness, tricking candidates into choosing a shorter duration or the wrong window type.
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 with .withAllowedLateness(Duration.standardMinutes(10))
`withAllowedLateness(Duration.standardMinutes(10))` on a fixed window allows late-arriving events to be included up to 10 minutes after the watermark passes the window's end. This directly meets the requirement to retain events arriving within 10 minutes of the watermark, while still using processing-time windows as specified.
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 sliding windows with no allowed lateness
Why it's wrong here
No allowed lateness drops all late data.
- ✓
Use fixed windows with .withAllowedLateness(Duration.standardMinutes(10))
Why this is correct
Allows late data up to 10 minutes after watermark.
- ✗
Use fixed windows with withAllowedLateness(Duration.standardSeconds(10))
Why it's wrong here
10 seconds is insufficient; need 10 minutes.
- ✗
Switch from processing time to event time and use default triggers
Why it's wrong here
Event time alone does not set allowed lateness; defaults are zero.
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