PMLE Monitoring ML Solutions Practice Question
An ML engineer is monitoring a model on Vertex AI Endpoint and sees that feature 'age' has a training distribution of (mean=45, std=10) but the serving distribution over the last hour shows (mean=30, std=15). JS divergence is 0.12, but the alert threshold is 0.1. The engineer suspects this is due to a temporary campaign targeting younger users. What should they do first?
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
✓
Use Vertex AI Explainable AI to understand the importance of the 'age' feature for the model.
Before taking action like retraining, the engineer should investigate the root cause. Using Vertex AI Explainable AI to check feature importance can confirm whether 'age' is a critical feature. If it is not important, the drift may be harmless.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Vertex AI Explainable AI to understand the importance of the 'age' feature for the model.
Why this is correct
Correct: understand feature importance to decide if drift matters.
- ✗
Immediately trigger retraining to avoid model degradation.
Why it's wrong here
Retraining should be based on root cause analysis, not immediate reaction.
- ✗
Increase the sampling rate to get more data before making a decision.
Why it's wrong here
More data might confirm drift but doesn't determine impact.
- ✗
Adjust the alert threshold to 0.2 to avoid false positives.
Why it's wrong here
Masking the alert doesn't address the drift; could ignore real issues.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.