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PDE Practice Question: Your company uses Vertex AI Pipelines to automate…
Your company uses Vertex AI Pipelines to automate the ML lifecycle. The pipeline includes training, evaluation, and deployment steps. You want to ensure that if a pipeline run fails due to a transient error (e.g., resource quota shortage), it automatically retries before marking the run as failed. What is the best way to implement this?
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
✓
In the pipeline component code, implement retry logic using exponential backoff for specific exceptions.
Vertex AI Pipelines does not have built-in retry logic for failed steps. You can wrap each step's logic to catch transient errors and retry, or use a retry mechanism in the container itself. Kubeflow Pipelines' retry policy can be specified. Modifying pipeline code is the most direct way.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure Vertex AI Pipelines to automatically restart failed runs.
Why it's wrong here
There is no such built-in configuration for automatic restart of failed runs.
- ✓
In the pipeline component code, implement retry logic using exponential backoff for specific exceptions.
Why this is correct
Retrying within the component handles transient failures gracefully without failing the entire pipeline.
- ✗
Set a high timeout value for the pipeline so that transient errors resolve before timeout.
Why it's wrong here
Timeout does not cause retries; it only waits longer for the same step to complete.
- ✗
Use Cloud Tasks to schedule pipeline runs and retry upon failure.
Why it's wrong here
Cloud Tasks is for asynchronous task execution, not for intra-pipeline retries; it would re-run the entire pipeline, not just the failed step.
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