Google Cloud Professional Machine Learning Engineer — Question 301

You developed an ML model using Vertex AI and deployed it to a Vertex AI endpoint. You anticipate that the model will need to be retrained as new data becomes available. You have configured a Vertex AI Model Monitoring Job. You need to monitor the model for feature attribution drift and establish continuous evaluation metrics. What should you do?

Answer options

• A. Set up alerts using Cloud Logging, and use the Vertex AI console to review feature attributions.
• B. Set up alerts using Cloud Logging, and use Looker Studio to create a dashboard that visualizes feature attribution drift. Review the dashboard periodically.
• C. Enable request-response logging for the Vertex AI endpoint, and set up alerts using Pub/Sub. Create a Cloud Run function to run TensorFlow Data Validation on your dataset.
• D. Enable request-response logging for the Vertex AI endpoint, and set up alerts using Cloud Logging. Review the feature attributions in the Google Cloud console when an alert is received.

Correct answer: D

Explanation

Option D is correct because it allows for real-time monitoring of feature attributions through Cloud Logging, enabling immediate action when an alert is triggered. Options A and B do not provide the necessary immediate response to alerts, as they rely on manual review or periodic checks. Option C introduces unnecessary complexity with a Cloud Run function and TensorFlow Data Validation, which is not required for monitoring feature attribution drift.