hardMultiple Choice
PMLE Practice Question: A company uses Vertex AI Pipelines to train and…
A company uses Vertex AI Pipelines to train and deploy models. The pipeline has a step that runs a custom container. The step fails intermittently with a timeout error. Which approach should be taken to robustly handle this?
⚠ Common exam trap
The trap here is that candidates may over-engineer the solution by choosing external retry mechanisms (Cloud Functions, Cloud Composer) or changing the pipeline framework, when the simplest and most correct fix is to adjust the step's timeout configuration within the pipeline definition itself.
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
✓
Increase the timeout for the step in the pipeline definition
Vertex AI Pipelines (built on Kubeflow Pipelines) allows you to define a `timeout` parameter for each pipeline step. Increasing this timeout directly addresses the intermittent timeout error by giving the custom container more time to complete its work, without changing the pipeline architecture or introducing external monitoring components. This is the most robust and minimal-change solution for a step that occasionally exceeds its current time limit.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to Kubeflow Pipelines
Why it's wrong here
Does not solve the timeout issue.
- ✗
Set up a Cloud Composer DAG to monitor and rerun the pipeline
Why it's wrong here
Overkill for a single step timeout.
- ✗
Reduce the size of the training data
Why it's wrong here
May affect model quality and not address cause.
- ✓
Increase the timeout for the step in the pipeline definition
Why this is correct
Raising the step timeout directly addresses the intermittent timeout by allowing the custom container more execution time before Vertex AI kills it. This satisfies the robustness requirement when the container legitimately needs longer than the default, without altering pipeline logic.
- ✗
Use Cloud Functions to retry the step
Why it's wrong here
Not integrated with Vertex Pipelines.
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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.