easyMultiple Choice
PDE Practice Question: Your company uses Cloud Dataflow to process…
Your company uses Cloud Dataflow to process streaming data from Pub/Sub. The pipeline occasionally fails with a 'worker terminated unexpectedly' error. What is the most likely cause of this error?
⚠ Common exam trap
Google Cloud often tests the distinction between infrastructure-level errors (like OOM) and configuration or permission errors, so candidates may incorrectly attribute the generic 'worker terminated' message to network or IAM issues rather than resource 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
✓
Insufficient memory per worker causing OOM errors
The 'worker terminated unexpectedly' error in Cloud Dataflow typically indicates that a worker process ran out of memory (OOM) and was killed by the operating system. This occurs when the pipeline's memory requirements exceed the configured worker machine type's memory capacity, often due to large windowing accumulations, skewed data, or inefficient state handling.
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 memory per worker causing OOM errors
Why this is correct
Worker termination typically results from the worker process being killed, and out-of-memory conditions are the common trigger: the JVM heap or container exceeds available memory, so the worker dies. Insufficient memory per worker directly explains the unexpected termination.
- ✗
Incorrect VPC firewall rules blocking internal communication
Why it's wrong here
VPC firewall rules blocking internal communication typically surface as connection timeouts or handshake failures, not abrupt worker termination. It is tempting because networking faults do disrupt streaming pipelines, and would be the answer if workers could not reach Pub/Sub or internal services, but the error indicates the worker process itself died.
- ✗
Staging location bucket lacks write permissions
Why it's wrong here
A staging bucket without write permissions fails the job at submission or during graph upload, before workers ever run. It is tempting because staging permissions are a common setup error, and would be correct if the pipeline failed immediately with an access-denied error, but it cannot produce intermittent worker termination mid-stream.
- ✗
Pub/Sub subscription throughput quota exceeded
Why it's wrong here
Exceeding the Pub/Sub subscription throughput quota causes backlogs and increased system lag, not worker process termination. It is tempting because quota exhaustion does degrade streaming pipelines, and would be the answer for sustained lag or throttling symptoms, but the reported error indicates worker resource exhaustion or out-of-memory kills.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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