hardMultiple ChoiceObjective-mapped
PDE Practice Question: A data science team uses Vertex AI Pipelines to…
A data science team uses Vertex AI Pipelines to automate retraining. They want to ensure that only models with performance above a threshold are deployed. Which component should they add to the pipeline?
⚠ Common exam trap
Watch out — candidates often confuse monitoring (Cloud Monitoring) or feature management (Feature Store) with the evaluation step needed to gate deployment, but only Model Evaluation provides the threshold-based conditional logic within the pipeline 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
✓
Vertex AI Model Evaluation
Vertex AI Model Evaluation provides built-in evaluation metrics and threshold-based validation that can be used as a pipeline condition to gate model deployment. By adding a Model Evaluation component, the pipeline can compare model performance against a predefined threshold and only proceed to deploy if the metrics (e.g., AUC, precision, recall) meet or exceed the required value.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertex AI Feature Store
Why it's wrong here
Used for feature management, not evaluation.
- ✓
Vertex AI Model Evaluation
Why this is correct
Evaluates model and can block deployment if threshold not met.
- ✗
Cloud Build trigger
Why it's wrong here
Cloud Build is for building containers.
- ✗
Cloud Monitoring alert
Why it's wrong here
Alerts are reactive, not pre-deployment gates.
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