PMLE Monitoring ML Solutions Practice Question
An ML engineer has deployed a model on Vertex AI Endpoints and wants to detect when the serving data distribution differs from the training data distribution. Which monitoring feature should they enable?
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
✓
Feature skew monitoring
Feature skew monitoring compares the training data distribution (stored in a baseline) with the serving data distribution to detect skew. Feature drift tracks changes over time in serving data only.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Prediction drift monitoring
Why it's wrong here
Prediction drift monitors changes in model predictions over time, not feature distributions.
- ✗
Feature drift monitoring
Why it's wrong here
Feature drift compares serving distribution over time, not vs training.
- ✗
Model quality monitoring
Why it's wrong here
Model quality monitoring requires ground truth labels to compare predictions vs actuals.
- ✓
Feature skew monitoring
Why this is correct
Correct: Feature skew compares training vs serving distributions.
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