PMLE Monitoring ML Solutions Practice Question
An MLOps team has deployed a model on Vertex AI Endpoints and wants to monitor for skew between training and serving data distributions. Which Vertex AI service should they use?
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 Monitoring
Vertex AI Model Monitoring is specifically designed for monitoring feature skew (training vs serving) and drift (serving over time) on deployed models.
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 Explainability
Why it's wrong here
Explainability provides feature attributions, not distribution monitoring.
- ✓
Vertex AI Model Monitoring
Why this is correct
Correct service for monitoring feature skew and drift.
- ✗
Vertex AI Model Registry
Why it's wrong here
Model Registry manages model versions and metadata.
- ✗
Vertex AI Continuous Training
Why it's wrong here
Continuous Training triggers retraining pipelines, not monitoring.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE 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 PMLE exam.