PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A team is using Vertex AI Model Registry to manage models. They need to ensure that when a new model version is registered, it is automatically evaluated for fairness and bias before being deployed. Which two Google Cloud services should they integrate to achieve this? (Choose two.)
⚠ Common exam trap
Many exam-takers confuse model monitoring with model evaluation; monitoring detects drift in deployed models, while evaluation computes fairness metrics for a model version.
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 Pipelines
Vertex AI Pipelines can orchestrate an automated workflow triggered by model registration, and Vertex AI Model Evaluation provides the fairness and bias metrics. Together, they enable pre-deployment evaluation. Other services like Model Monitoring and Feature Store focus on different aspects and cannot perform fairness assessment at registration time.
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 Pipelines
Why this is correct
Vertex AI Pipelines can orchestrate a workflow that triggers upon model registration, running evaluation steps such as fairness and bias checks. It allows integration with other services and custom code, enabling automated pre-deployment assessment. By using pipelines, the team can enforce that no model is deployed without passing the fairness evaluation.
- ✗
Vertex AI Feature Store
Why it's wrong here
Vertex AI Feature Store is used for managing and serving features, not for evaluating model fairness. It can provide feature statistics, but it does not compute bias metrics or assess model predictions. Integrating Feature Store would not fulfill the requirement to evaluate fairness and bias automatically upon model registration.
- ✓
Vertex AI Model Evaluation
Why this is correct
Vertex AI Model Evaluation provides built-in fairness and bias metrics for models. It can be invoked within a pipeline to compute metrics like disparate impact and equal opportunity. This service directly addresses the requirement to evaluate fairness before deployment, and its results can gate the deployment step.
- ✗
Cloud Build
Why it's wrong here
Cloud Build is a CI/CD service that can automate building and deploying containers, but it does not have native capabilities for fairness or bias evaluation. While it could trigger a script, it lacks the specialized ML evaluation features. Using Cloud Build alone would require custom implementation and would not provide the integrated fairness metrics.
- ✗
Vertex AI Model Monitoring
Why it's wrong here
Vertex AI Model Monitoring is designed to detect drift and anomalies in deployed models, not to evaluate fairness or bias during registration. It operates on live traffic and requires a deployed endpoint, so it cannot be used pre-deployment to assess a model version. This service does not provide fairness metrics or bias detection capabilities.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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