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PMLE Practice Question: A company uses Vertex AI Pipelines to orchestrate…
A company uses Vertex AI Pipelines to orchestrate their ML training workflow. The pipeline includes a BigQuery ML training step, a model evaluation step, and a deployment step to Vertex AI Endpoints. The engineer notices that the pipeline fails intermittently due to a quota exceeded error on Vertex AI Endpoints during model deployment. What is the best long-term solution to prevent this 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
✓
Add retry logic with exponential backoff to the deployment step in the pipeline.
Adding retry logic with exponential backoff handles transient quota errors gracefully without manual intervention, making it a resilient and long-term solution for intermittent failures. Option A is wrong because longer wait times do not address the root cause of quota exhaustion. Option C is wrong because deploying on Compute Engine introduces additional complexity and does not resolve Vertex AI quota issues. Option D is wrong because requesting a permanent quota increase may be necessary for sustained high volume, but it requires planning and approval, and is not the best first step for intermittent errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run the pipeline steps sequentially with longer wait times.
Why it's wrong here
Running steps sequentially with longer wait times does not resolve quota exceeded errors because the issue is not about timing but about hitting a resource limit. This approach may even increase the chance of failure due to prolonged pipeline execution.
- ✓
Add retry logic with exponential backoff to the deployment step in the pipeline.
Why this is correct
Adding retry logic with exponential backoff is the best long-term solution because it gracefully handles transient quota errors by waiting and retrying, reducing the chance of failure without requiring manual intervention or permanent changes.
- ✗
Switch to deploying models using a custom container on Compute Engine.
Why it's wrong here
Switching to Compute Engine does not address the underlying Vertex AI Endpoints quota issue; it only avoids using Endpoints, which may not be desirable for serving ML models with Vertex AI's managed infrastructure.
- ✗
Request a permanent quota increase for Vertex AI Endpoints.
Why it's wrong here
Requesting a permanent quota increase does not address the root cause of intermittent failures, which is likely a transient resource contention spike during deployment; a permanent increase wastes quota during idle periods and does not implement retry logic or exponential backoff to handle temporary limits. This option is tempting because a quota increase directly resolves persistent, hard capacity ceilings, and would be correct if the failure were consistently caused by hitting a fixed, insufficient limit during every deployment attempt.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.