PMLE Monitoring ML Solutions Practice Question
A team is monitoring a deployed model and notices that the prediction distribution has changed significantly over the last week. They want to detect which features are contributing most to the drift. Which tool should they use?
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
✓
Vertex AI Explainable AI
Vertex AI Explainable AI provides feature attributions (e.g., SHAP values) that can be used to identify which features are most important for predictions. By comparing feature importance over time, they can pinpoint which features are drifting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Vertex AI Explainable AI
Why this is correct
Explainable AI provides feature attributions (SHAP, integrated gradients) that can help identify which features are drifting.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store stores features but does not analyze drift contributions.
- ✗
Vertex AI Model Monitoring
Why it's wrong here
Model Monitoring detects drift but does not directly provide per-feature importance for root cause analysis.
- ✗
Vertex AI Pipelines
Why it's wrong here
Pipelines orchestrate ML workflows but do not provide drift root cause analysis.
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Same concept, more angles
2 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist notices that the prediction distribution of a deployed model has changed significantly over the past week. They want to identify which features are contributing most to the drift. Which approach should they use?
medium- ✓ A.Use Vertex AI Explainability to get feature importance and identify which features have high importance and significant drift
- B.Compute Pearson correlation between each feature's drift score and the model's prediction drift
- C.Use Vertex AI Model Monitoring to compare training and serving distributions for each feature
- D.Enable request/response logging to BigQuery and manually analyze feature distributions
Why A: Vertex AI Explainability provides feature attributions (e.g., SHAP values) that can be used to correlate feature drift with model prediction changes.
Variation 2. A company has deployed a model to Vertex AI Endpoints and wants to monitor for feature drift using Jensen-Shannon divergence. They have set a threshold of 0.1. After one week, the monitoring job reports a divergence of 0.15 for a feature. What should the engineer do next to diagnose which features are contributing to the drift?
medium- A.Deploy a new model version immediately
- ✓ B.Use Vertex AI Explainability to compute feature attributions and identify drifted features
- C.Check the model's confusion matrix in BigQuery
- D.Increase the sampling rate to capture more data
Why B: To identify which features are drifting, the engineer can use Vertex AI Explainability to compute feature attributions (e.g., SHAP values) and correlate them with drift metrics.
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.