easyMultiple Choice
PDE Practice Question: A company uses Dataflow to process streaming data…
A company uses Dataflow to process streaming data from Pub/Sub. They notice increased processing latency. What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the misconception that Pub/Sub subscription issues (like ack deadline) are the primary cause of latency, but the trap here is that latency in Dataflow is almost always a worker scaling problem, not a Pub/Sub configuration issue.
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
✓
Insufficient workers
In Dataflow, processing latency increases most commonly due to insufficient workers, as the streaming pipeline cannot keep up with the incoming data rate when the number of Compute Engine instances is too low. This causes backpressure from Pub/Sub, leading to growing unacknowledged messages and higher end-to-end latency. Autoscaling may be delayed or limited by max worker count settings, making manual or configuration-based worker scaling the primary corrective action.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Insufficient workers
Why this is correct
Insufficient workers create backpressure and increased latency as the pipeline cannot keep up with throughput.
- ✗
Pub/Sub subscription issue
Why it's wrong here
Subscription issues would cause missing data or ingestion errors, not necessarily increased processing latency.
- ✗
Too many shards
Why it's wrong here
Too many shards can cause overhead, but it's less common than insufficient workers.
- ✗
Wrong machine type
Why it's wrong here
Machine type affects per-machine performance, but insufficient workers is more likely to cause increased latency.
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