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PDE Practice Question: Your team is using Vertex AI Pipelines to…
Your team is using Vertex AI Pipelines to orchestrate a model retraining workflow. The pipeline includes a data validation step, a training step, and a model evaluation step. You want to ensure that if the evaluation step fails due to low model performance, the pipeline stops and does not deploy the model. Which approach should you use?
⚠ Common exam trap
Test-takers frequently confuse retry logic (Option B) with conditional gating, mistakenly thinking that retrying a failed evaluation step will somehow improve model performance, when in fact retries only handle transient errors, not metric-based failures.
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
✓
Use a Conditional in the pipeline to check evaluation metrics and only run the deployment step if metrics pass thresholds
Vertex AI Pipelines supports conditional execution via the `Condition` component, which allows you to evaluate model performance metrics (e.g., accuracy, RMSE) and gate subsequent steps. By placing the deployment step inside a conditional branch that only executes when evaluation metrics meet predefined thresholds, the pipeline automatically stops and avoids deploying a poor-performing model. This approach aligns with MLOps best practices for automated gating in production pipelines.
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 evaluation step after deployment and roll back if performance is low
Why it's wrong here
This would deploy a poorly performing model before rollback.
- ✗
Configure the evaluation step to retry up to 3 times on failure
Why it's wrong here
Retrying does not address low model performance.
- ✓
Use a Conditional in the pipeline to check evaluation metrics and only run the deployment step if metrics pass thresholds
Why this is correct
Conditionals allow pipeline to branch based on results.
- ✗
Create a separate pipeline for deployment and trigger it manually after review
Why it's wrong here
Manual trigger is not automated and may cause delays.
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