PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A company wants to monitor features in Vertex AI Feature Store for drift over time. Which two services should they use? (Choose two.)
⚠ Common exam trap
PMLE often tests the distinction between Feature Store monitoring (feature-level drift) and Model Monitoring (prediction-level drift/skew), so candidates who conflate the two pick Vertex AI Model Monitoring.
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 Feature Store monitoring
Option A, Vertex AI Feature Store monitoring, is correct because it is the native capability that computes feature-level drift and skew statistics for features served from a Vertex AI Feature Store, generating metrics and alerts when distributions shift over time. Option E, Cloud Monitoring, is correct because the drift metrics produced by Feature Store monitoring are exported to Cloud Monitoring, where they can be visualized on dashboards and used to create alerting policies. Option B, Cloud Logging, is not the right choice because it captures log entries rather than computing or charting feature drift metrics. Option C, Vertex AI Model Monitoring, targets model prediction drift/skew on deployed endpoints, not feature drift within a Feature Store. Option D, Vertex AI Experiments, is for tracking training runs, parameters, and metrics, not for ongoing drift monitoring of stored features.
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 monitoring
Why this is correct
Vertex AI Feature Store monitoring computes drift and skew metrics natively against feature values, detecting distribution shifts over time without custom code. It satisfies the requirement to monitor features for drift directly within the feature store, providing scheduled drift detection and alerting on the stored feature data.
- ✗
Cloud Logging
Why it's wrong here
Cloud Logging records and stores log entries from Google Cloud services; it does not compute feature drift statistics or distributions. It is tempting because logs can be queried and alerted on, and would be correct for auditing events or debugging service errors rather than statistical drift detection.
- ✗
Vertex AI Model Monitoring
Why it's wrong here
Vertex AI Model Monitoring detects skew and drift for deployed models' prediction inputs, not for Feature Store feature values themselves. It is tempting because drift detection is its core purpose, and would be correct for monitoring a model endpoint's serving traffic after deployment.
- ✗
Vertex AI Experiments
Why it's wrong here
Vertex AI Experiments tracks training runs, parameters and metrics for model development, not feature drift in a Feature Store. It is tempting because it provides monitoring-style visibility into model behaviour, and would be correct for comparing experiment results during model iteration.
- ✓
Cloud Monitoring
Why this is correct
Cloud Monitoring ingests the drift and skew metrics that Vertex AI Feature Store monitoring emits, letting teams build dashboards, set alerting policies and notify on threshold breaches. It satisfies the ongoing monitoring requirement by surfacing drift signals operationally rather than only computing them.
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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
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.