easyMultiple Choice
PMLE Practice Question: An ML engineer needs to monitor a deployed model…
An ML engineer needs to monitor a deployed model for data drift. They want to compare the distribution of incoming predictions against a baseline distribution. Which Vertex AI service should they use?
⚠ Common exam trap
Google Cloud often tests the distinction between monitoring (drift detection) and other MLOps components like feature stores or experiment tracking, so the trap here is that candidates may confuse 'monitoring' with 'storing features' or 'tracking experiments' because all are part of the ML lifecycle but serve different purposes.
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 the correct service because it is specifically designed to detect data drift and feature skew in deployed models. It continuously compares the distribution of incoming prediction requests against a baseline distribution (e.g., training data or a previous window) and alerts the engineer when statistically significant drift is detected, using metrics like Jensen-Shannon divergence or L-infinity distance.
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 versions feature values for training and online inference; it does not compare incoming prediction distributions against a baseline. It is tempting because both sit in the serving path, but drift detection requires Vertex AI Model Monitoring with a configured training baseline.
- ✓
Vertex AI Model Monitoring
Why this is correct
Vertex AI Model Monitoring computes drift metrics by comparing incoming prediction request distributions against a baseline dataset, covering both feature and prediction drift. It directly satisfies the requirement to detect distribution shift on a deployed model.
- ✗
Vertex AI Experiments
Why it's wrong here
Vertex AI Experiments tracks training runs, parameters, metrics and artefacts for reproducibility; it stores no serving-time prediction distributions. It is tempting because both involve metrics over time, but drift detection needs Vertex AI Model Monitoring, which compares live traffic against a baseline distribution.
- ✗
Vertex AI Explainable AI
Why it's wrong here
Explainable AI attributes individual predictions to input features via feature attributions; it does not compute distribution comparisons against a baseline. It is tempting because both concern model behaviour monitoring, but drift detection requires Vertex AI Model Monitoring with a training or skew baseline configured.
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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.