PDE Maintaining and Automating Data Workloads Practice Question
You are running a streaming pipeline with Dataflow that reads from Pub/Sub and writes to BigQuery. You notice that the system lag metric is increasing over time, indicating that messages are taking longer to process. What is the most likely cause and how should you address it?
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
✓
The Dataflow workers are CPU-bound; increase the number of workers or adjust autoscaling settings.
Increasing system lag often means the pipeline is CPU-bound, failing to keep up with the incoming data rate. Updating the pipeline with a higher number of workers (or enabling autoscaling) can resolve this.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The source Pub/Sub topic has insufficient throughput; increase the number of partitions.
Why it's wrong here
Pub/Sub auto-scales; system lag in Dataflow indicates processing bottleneck, not source throughput.
- ✓
The Dataflow workers are CPU-bound; increase the number of workers or adjust autoscaling settings.
Why this is correct
High system lag suggests worker resources are insufficient; adding workers reduces lag.
- ✗
The BigQuery destination table has too many columns; reduce the number of columns.
Why it's wrong here
Column count does not typically cause system lag; it's a processing latency issue.
- ✗
The pipeline uses a batch transform that should be replaced with a streaming transform.
Why it's wrong here
Streaming transforms are already used; system lag is about resource contention, not transform type.
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