easyMultiple Choice
PDE Practice Question: A data pipeline ingests streaming data from…
A data pipeline ingests streaming data from Pub/Sub into BigQuery via Dataflow. Recently, the pipeline has been failing with 'deadline exceeded' errors. What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the distinction between resource quota errors (like BigQuery streaming quota) and Pub/Sub-specific timeout errors, trapping candidates who confuse 'deadline exceeded' with general quota exhaustion.
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 Pub/Sub subscription's acknowledgement deadline is too short for the processing time.
'deadline exceeded' errors in a Dataflow pipeline reading from Pub/Sub indicate that the subscriber is taking longer to process messages than the acknowledgement deadline allows. When the deadline expires, Pub/Sub redelivers the message, causing duplicate processing and eventual pipeline failure. This is a common issue when processing time exceeds the default 10-second acknowledgement deadline.
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 BigQuery streaming quota is exceeded.
Why it's wrong here
Exceeding the BigQuery streaming quota returns quota or rateLimitExceeded errors, not deadline exceeded. This option would be correct if the pipeline logged 403 quota failures during high-volume inserts rather than timeout exceptions from the Dataflow worker.
- ✗
Dataflow workers are underutilized due to batch size settings.
Why it's wrong here
Underutilised workers with small batches cause lower throughput, not deadline exceeded errors, which arise when calls exceed their timeout. It is tempting because batch tuning affects pipeline performance, and it would be correct for latency or cost issues rather than timeout failures.
- ✗
Dataflow autoscaling is disabled.
Why it's wrong here
Autoscaling adjusts worker counts to match throughput, but deadline exceeded arises when individual bundles exceed the configured timeout, not from insufficient parallelism. Autoscaling would be the answer if the pipeline were lagging behind Pub/Sub backlog with healthy workers.
- ✓
The Pub/Sub subscription's acknowledgement deadline is too short for the processing time.
Why this is correct
Pub/Sub redelivers a message when its acknowledgement deadline expires before the subscriber acknowledges it; Dataflow then surfaces deadline exceeded errors. If processing consistently takes longer than the configured deadline, the subscription's acknowledgement window is too short for the actual processing time.
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