hardMultiple ChoiceObjective-mapped
PDE Practice Question: A Dataflow streaming pipeline that uses global…
A Dataflow streaming pipeline that uses global windows and triggers every 5 seconds is experiencing increasing lag and high system latency. The pipeline reads from Pub/Sub, transforms data with a ParDo, and writes to BigQuery. Which action is most likely to reduce lag?
⚠ Common exam trap
Google Cloud often tests the misconception that increasing workers or changing trigger timing alone can fix lag caused by inappropriate windowing strategy, when the real issue is that global windows with frequent triggers create unbounded state that overwhelms the pipeline's memory and shuffle capacity.
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
✓
Replace the global window with a sliding window of 1 minute.
B is correct because sliding windows of 1 minute allow the pipeline to process data in overlapping fixed-size windows, which can reduce the buildup of data in memory compared to global windows. Global windows with frequent triggers (every 5 seconds) can cause unbounded state growth and high latency as the pipeline must maintain state for all elements until the trigger fires, whereas sliding windows naturally bound the data per window and enable more efficient watermark and trigger management in 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 a session window to group related events.
Why it's wrong here
Session windows are useful for user sessions but may not reduce overall lag in this scenario.
- ✓
Replace the global window with a sliding window of 1 minute.
Why this is correct
A sliding window reduces the number of elements per trigger and improves latency by distributing state across workers.
- ✗
Change the trigger to processing time instead of event time.
Why it's wrong here
This could worsen lag if events are out of order; the root cause is windowing strategy.
- ✗
Increase the number of workers manually.
Why it's wrong here
Adding workers may help but does not address the root cause of windowing overhead.
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