PMLE Monitoring ML Solutions Practice Question
An ML engineer manages a Vertex AI Endpoint serving a recommendation model. The team wants to detect when the distribution of a specific numerical feature, average session duration, shifts significantly from its training distribution. They have configured Vertex AI Model Monitoring with a training dataset baseline and a monitoring frequency of one hour. After a week, no drift alerts have fired even though the feature's daily mean has visibly moved. What is the most likely cause?
⚠ Common exam trap
The trap here is focusing on frequency or baseline mechanics while overlooking that unlisted features are simply not monitored.
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
✓
The feature was not included in the monitoring configuration's feature list, so it is not being analyzed.
Vertex AI Model Monitoring only evaluates features that are explicitly included in the monitoring configuration. If average session duration was left out of the monitored feature list, drift for that feature is never computed, so no alert can fire even when the mean shifts. The other options either describe non-existent behavior or misattribute the cause to frequency or baseline updates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The monitoring frequency of one hour is too infrequent to detect daily mean shifts.
Why it's wrong here
One-hour frequency is more than sufficient to detect a daily mean shift; in fact it is quite granular. Frequency controls how often analysis runs, not whether a feature is analyzed. If the feature were monitored, hourly analysis would have caught the change. The absence of alerts is not explained by the schedule.
- ✗
Drift alerts require at least 30 days of live data before they can trigger.
Why it's wrong here
There is no 30-day warm-up requirement for drift alerts in Vertex AI Model Monitoring. Alerts can fire as soon as enough live samples are collected within a monitoring window, which is typically much shorter. This option invents a constraint that does not exist, so it does not explain why no alerts fired after a week of visible movement.
- ✗
The training dataset baseline was overwritten by the latest live data automatically.
Why it's wrong here
Vertex AI Model Monitoring does not automatically overwrite a training dataset baseline with live data. The baseline remains static until explicitly updated. This option describes behavior that does not occur, so it cannot explain the missing alerts. The real issue is more likely a configuration omission rather than an automatic baseline change.
- ✓
The feature was not included in the monitoring configuration's feature list, so it is not being analyzed.
Why this is correct
Vertex AI Model Monitoring only analyzes features explicitly listed in the monitoring configuration. If average session duration was omitted from the monitored feature list, the service will not compute drift for it regardless of the baseline or frequency. This is the most likely reason no alerts fired despite an obvious shift, because unlisted features are silently ignored.
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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.