easyMultiple ChoiceObjective-mapped
PDE Practice Question: Operating a streaming data pipeline that uses…
You are operating a streaming data pipeline that uses Cloud Pub/Sub and Dataflow. The data source sometimes emits events that are delayed by several minutes due to network issues. Your pipeline must produce accurate aggregations (e.g., counts per minute) even for late data, but you also need to avoid waiting for a long time before emitting results. Which approach should you use?
⚠ Common exam trap
Google Cloud often tests the distinction between processing-time and event-time semantics, and the trap here is that candidates may choose processing-time windows (Option A) thinking they are simpler, not realizing they sacrifice correctness for 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 event-time processing with allowed lateness and a trigger that fires early to provide speculative results.
It uses event-time processing to handle late data via allowed lateness, combined with early triggers to emit speculative results before the window closes. This balances accuracy for delayed events with low latency for downstream consumers, which is a common requirement in streaming pipelines using Cloud Pub/Sub and Dataflow.
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 processing-time windows and ignore the event timestamps entirely.
Why it's wrong here
Processing-time windows conflate the true timing of events, leading to incorrect aggregations.
- ✓
Use event-time processing with allowed lateness and a trigger that fires early to provide speculative results.
Why this is correct
Dataflow supports allowed lateness and triggers; you can set a trigger to emit early results every minute, and then a final result after the allowed lateness period, ensuring both low latency and eventual accuracy.
- ✗
Use global windows and hold all data for 24 hours before processing to ensure completeness.
Why it's wrong here
Holding data for 24 hours introduces unacceptable delay for real-time insights.
- ✗
Use event-time processing and discard any data that arrives after the window ends.
Why it's wrong here
Discarding late data reduces accuracy, which is not acceptable.
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