PMLE Monitoring ML Solutions Practice Question
A fraud detection model is deployed to a Vertex AI Endpoint and configured with Vertex AI Model Monitoring for feature drift. The team wants the drift monitor to compare live production traffic against the exact statistics captured from the training dataset, so that alerts reflect deviation from the model's original data distribution rather than from recent traffic. Which configuration should they use?
⚠ Common exam trap
The trap here is assuming that any monitoring configuration with drift enabled will automatically compare against training data, when the baseline must be explicitly specified.
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
✓
Set the monitoring training dataset to the original training data and enable drift detection.
Feature drift detection in Vertex AI Model Monitoring requires a baseline distribution to compare against. Using the original training data as the baseline anchors the comparison to the model's training distribution, which is exactly what the team wants. Other options either change the comparison window, switch to prediction drift, or alter scheduling without defining the reference, so they do not meet the stated requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable prediction drift instead of feature drift and let Vertex AI infer the baseline automatically.
Why it's wrong here
Prediction drift monitors changes in output distributions, not input feature distributions against training statistics. While Vertex AI can infer baselines in some configurations, prediction drift answers a different question and would not compare live feature values to the original training data. The scenario requires feature-level comparison to training statistics.
- ✗
Configure the monitor to use a rolling 24-hour window of live requests as the baseline distribution.
Why it's wrong here
A rolling live baseline measures change between adjacent production windows, not deviation from the training distribution. It can mask gradual drift that accumulates slowly, and it will not alert on a shift that occurred before the window began. The team explicitly wants comparison against training statistics, so a live rolling window does not satisfy the requirement.
- ✗
Attach the training dataset as a Vertex AI managed dataset and set the monitoring frequency to daily.
Why it's wrong here
Attaching a managed dataset or changing monitoring frequency does not by itself define the reference distribution used for drift scoring. Frequency controls how often analysis runs, while the baseline is what the live data is compared against. Without specifying the reference dataset as the baseline, the monitor will not compare against training statistics.
- ✓
Set the monitoring training dataset to the original training data and enable drift detection.
Why this is correct
Vertex AI Model Monitoring computes drift by comparing live feature distributions against a baseline derived from a training dataset or a saved baseline. Pointing the monitor at the original training data fixes the reference distribution to the model's training period, so drift alerts reflect deviation from that stable baseline rather than from rolling production windows.
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JA
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.