PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
An ML team wants to monitor feature drift in their production model. Which Vertex AI Feature Store capability should they use?
⚠ Common exam trap
PMLE often tests the confusion between feature views (serving constructs) and feature monitoring (drift detection), so candidates pick feature views when the question asks specifically about drift.
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
✓
Feature monitoring (drift detection)
Feature monitoring (drift detection) is the Vertex AI Feature Store capability that computes drift and skew metrics for feature values against a baseline and emits them for alerting. It is purpose-built to detect when production feature distributions diverge from training or reference distributions. This directly answers the requirement to monitor feature drift.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feature views
Why it's wrong here
Feature views define reusable, versioned selections of features for training and serving, not drift measurement. Drift monitoring in Vertex AI is performed by Model Monitoring, which compares production feature distributions against a training baseline. Feature views would be the right choice for curating a consistent feature set across models.
- ✗
Online store
Why it's wrong here
The online store serves low-latency feature values for real-time prediction; it does not compute drift statistics. Drift monitoring requires Model Monitoring, which samples served features and compares them with the training baseline. The online store is the right choice when predictions must fetch fresh feature values within milliseconds.
- ✗
Point-in-time retrieval
Why it's wrong here
Point-in-time retrieval returns historical feature values as of a timestamp to prevent training-serving skew, not to detect distributional drift. Drift detection compares live serving statistics against a baseline via Model Monitoring. Point-in-time retrieval is correct when building training datasets that must reflect feature values at each label's event time.
- ✓
Feature monitoring (drift detection)
Why this is correct
Feature monitoring in Vertex AI Feature Store continuously computes drift metrics by comparing production feature distributions against a baseline snapshot, directly satisfying the requirement to monitor feature drift. It detects skew and drift per feature, emitting alerts without retraining, which is precisely the capability the stem requests.
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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.