hardMultiple Choice
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
Feature Store serves and manages feature data for training and online serving; it holds no evaluation results and cannot branch a pipeline on accuracy. It is the right component when features must be shared consistently between training and prediction.
- ✓
Vertex AI Model Evaluation
Why this is correct
Vertex AI Model Evaluation computes metrics against a dataset, producing an evaluation resource. Adding it to the pipeline lets a conditional deployment step compare those metrics to the threshold, so only models meeting the performance bar are deployed.
- ✗
Cloud Build trigger
Why it's wrong here
Cloud Build triggers start builds in response to repository or webhook events; they cannot evaluate model metrics or gate pipeline execution. They are the right component for compiling, testing and deploying application code when a commit is pushed.
- ✗
Cloud Monitoring alert
Why it's wrong here
Cloud Monitoring alerts notify humans after a metric breaches a threshold; they cannot gate pipeline execution or block deployment. The pipeline needs a programmatic conditional step, such as a custom evaluation component, that inspects metrics and halts or proceeds. Alerts suit ongoing production surveillance, not pre-deployment quality control within Vertex AI Pipelines.
Go deeper
Related to this question
About these practice questions
One of 747 original PDE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.