PMLE Monitoring ML Solutions Practice Question

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?

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 Explainability to get feature importance and identify which features have high importance and significant drift

Vertex AI Explainability provides feature attributions (e.g., SHAP values) that can be used to correlate feature drift with model prediction changes.

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 Explainability to get feature importance and identify which features have high importance and significant drift
Why this is correct
Combining drift detection with feature importance pinpoints root cause features.
✗
Compute Pearson correlation between each feature's drift score and the model's prediction drift
Why it's wrong here
Pearson correlation may not capture non-linear relationships.
✗
Use Vertex AI Model Monitoring to compare training and serving distributions for each feature
Why it's wrong here
Model Monitoring only detects drift, not root cause analysis.
✗
Enable request/response logging to BigQuery and manually analyze feature distributions
Why it's wrong here
Manual analysis is not scalable; this approach lacks automation.

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PMLE Monitoring ML Solutions Practice Question

Why each option matters

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