hardMultiple Choice
PDE Practice Question: A Dataflow pipeline as described in the exhibit…
Exhibit
Refer to the exhibit. Exhibit: Pipeline description: - Source: PubSubIO.read() - Transform: ParDo(Process) - Window: Window.into(FixedWindows of 1 minute) - Transform: GroupByKey - Sink: Write to BigQuery using StreamingInserts - Estimated throughput: 10MB/s - Observed lag: increasing
A Dataflow pipeline as described in the exhibit has increasing lag. Which optimization is most likely to reduce the lag?
⚠ Common exam trap
Google Cloud often tests the misconception that scaling workers or changing windowing fixes all performance issues, but the trap here is that the lag is specifically caused by the BigQuery sink's streaming insert throttling, which requires a sink-level optimization like FileLoads.
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 FileLoads instead of StreamingInserts for BigQuery output
The exhibit shows increasing lag in a Dataflow pipeline writing to BigQuery. StreamingInserts (the default) use the BigQuery Storage Write API, which can throttle under high throughput, causing backpressure and lag. Switching to FileLoads writes data to temporary files in Cloud Storage and then loads them into BigQuery via batch load jobs, which decouples the write path from the streaming insert quota and reduces lag by avoiding per-row insert limits.
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 FileLoads instead of StreamingInserts for BigQuery output
Why this is correct
FileLoads (batch loads) are more efficient for high throughput and reduce lag.
- ✗
Increase the number of workers
Why it's wrong here
More workers may help but the streaming insert bottleneck remains.
- ✗
Use global windows instead of fixed windows
Why it's wrong here
Global windows would increase latency as data waits until end.
- ✗
Add additional ParDo transforms
Why it's wrong here
More transforms add processing overhead, increasing lag.
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