PMLE Monitoring ML Solutions Practice Question
An engineer wants to configure alerting when the data distribution of a serving feature deviates from the training data distribution. The model is deployed on Vertex AI Endpoints. Which divergence metric should they use to compare the training and serving distributions?
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
✓
Jensen-Shannon divergence
Vertex AI Model Monitoring supports Jensen-Shannon divergence for comparing distributions. It is a symmetric and bounded metric suitable for detecting feature skew between training and serving data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Kullback-Leibler divergence
Why it's wrong here
KL divergence is not symmetric and can be unstable; Vertex AI uses JS divergence as the default.
- ✗
Population Stability Index (PSI)
Why it's wrong here
PSI is available but more commonly used for monitoring drift over time, not specifically for training-serving skew.
- ✓
Jensen-Shannon divergence
Why this is correct
JS divergence is the recommended metric for detecting distribution skew in Vertex AI Model Monitoring.
- ✗
Chi-squared test
Why it's wrong here
Chi-squared is a hypothesis test, not a divergence metric directly used in Vertex AI Model Monitoring.
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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.