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PMLE Practice Question: A company has a Vertex AI pipeline that trains a…
A company has a Vertex AI pipeline that trains a model on streaming data from Pub/Sub. The pipeline is triggered by a Cloud Function when new data arrives. Recently, jobs have been failing with 'ResourceExhausted: Quota limit exceeded for regional CPUs in us-central1.' The team needs to ensure successful job execution while minimizing changes. Which approach should they take?
⚠ Common exam trap
Google Cloud often tests the misconception that rate limiting (Option C) solves quota exhaustion, but the trap here is that quota limits are per-resource (e.g., regional CPUs) and rate limiting does not change the per-job resource consumption, so it only delays the inevitable failure.
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
✓
Configure the pipeline's training job to use preemptible VMs (which count toward a separate, usually higher quota).
Preemptible VMs count toward a separate, often higher quota for 'Preemptible CPUs' rather than the standard regional CPU quota. By configuring the training job to use preemptible VMs, the team can bypass the exhausted quota without requesting a limit increase or changing the pipeline architecture. This minimizes changes while leveraging the fact that Vertex AI training jobs can be configured to use preemptible VMs via the `worker_pool_specs` with `accelerator_type` and `machine_type` settings.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Request a quota increase from Google Cloud Support.
Why it's wrong here
This is a valid long-term fix but not minimal; it requires intervention.
- ✗
Change the pipeline to run in a different region with available quota.
Why it's wrong here
This may require data movement and is not minimal change.
- ✗
Reduce the number of parallel pipeline runs by using a Cloud Tasks queue with rate limiting.
Why it's wrong here
This doesn't help if quota is already exhausted; it just slows down.
- ✓
Configure the pipeline's training job to use preemptible VMs (which count toward a separate, usually higher quota).
Why this is correct
Preemptible VMs have a separate quota and are cheaper.
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