PMLE Monitoring ML Solutions Practice Question
A model deployed on a Vertex AI Endpoint uses an image model with XRAI explainability. The team notices that the prediction distributions are shifting over time. They want to monitor prediction drift. However, the explainability feature is not enabled. What must the engineer do to enable monitoring prediction drift?
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
✓
Configure Vertex AI Model Monitoring to monitor prediction drift
Prediction drift monitoring is part of Vertex AI Model Monitoring and does not require explainability to be enabled. It can be configured independently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Re-deploy the model with a sampling rate of 100%
Why it's wrong here
Sampling rate affects data capture but does not enable drift monitoring.
- ✓
Configure Vertex AI Model Monitoring to monitor prediction drift
Why this is correct
Correct: Prediction drift monitoring is a built-in feature of Model Monitoring.
- ✗
Enable Vertex AI Explainability with XRAI on the endpoint deployment
Why it's wrong here
Explainability is not required for prediction drift monitoring.
- ✗
Enable request/response logging to BigQuery and build custom drift detection
Why it's wrong here
Enabling request/response logging to BigQuery and building custom drift detection is tempting because it allows for granular analysis of prediction behaviour. However, it fails in this specific scenario because, without the explainability feature enabled, the logged responses lack the necessary XRAI data to directly monitor prediction drift *using explainability*. This approach would be correct if the goal was general logging and custom analysis of raw predictions, not specifically leveraging XRAI for drift monitoring.
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