PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
You are setting up feature monitoring in Vertex AI Feature Store to detect drift in a numerical feature. The monitoring job should run daily and alert if the Jensen-Shannon divergence exceeds 0.1. Which configuration should you use?
⚠ Common exam trap
The trap is assuming model monitoring on the endpoint covers feature drift — PMLE candidates often pick endpoint monitoring when the question is specifically about Feature Store feature-level drift detection.
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
✓
Configure feature monitoring in the feature view with a drift threshold of 0.1 using Jensen-Shannon divergence
Vertex AI Feature Store supports native feature monitoring configured at the feature view level, where you specify the drift detection method (including Jensen-Shannon divergence) and a threshold. Setting the threshold to 0.1 with a daily schedule directly satisfies the requirement without building custom infrastructure. This is the first-party, managed approach for detecting 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.
- ✓
Configure feature monitoring in the feature view with a drift threshold of 0.1 using Jensen-Shannon divergence
Why this is correct
Configuring monitoring on the feature view applies drift detection directly to the served feature data, satisfying the daily Jensen-Shannon divergence threshold of 0.1. Feature-view-level monitoring evaluates the numerical feature against its baseline distribution, so alerts trigger precisely when divergence exceeds the specified limit.
- ✗
Use BigQuery scheduled queries to compare distributions and send alerts
Why it's wrong here
BigQuery scheduled queries compute distributions but cannot register feature monitoring or emit Vertex AI Feature Store drift alerts against a Jensen-Shannon threshold. They suit bespoke SQL analytics on warehouse tables, where no native feature-monitoring resource is required.
- ✗
Set up a Cloud Composer DAG to compute drift and publish to Cloud Monitoring
Why it's wrong here
Cloud Composer orchestrates arbitrary workflows but lacks native drift detection for Vertex AI Feature Store; the correct approach uses Vertex AI’s built-in monitoring configuration, which directly computes Jensen-Shannon divergence and triggers alerts without custom DAG coding. This option tempts because Composer excels at scheduled batch pipelines, and for custom drift calculations on non-Vertex AI data it would be valid, but here the requirement demands Vertex AI’s native feature monitoring.
- ✗
Enable model monitoring on the Vertex AI endpoint to detect drift
Why it's wrong here
Endpoint model monitoring tracks prediction drift on deployed models, not feature-level distributions in Feature Store. It would be the right choice when serving predictions and wanting skew or drift alerts on a deployed model's traffic, not for monitoring a stored numerical feature's Jensen-Shannon divergence.
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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.