Google Cloud Professional Machine Learning Engineer — Question 173

You developed a custom model by using Vertex AI to forecast the sales of your company’s products based on historical transactional data. You anticipate changes in the feature distributions and the correlations between the features in the near future. You also expect to receive a large volume of prediction requests. You plan to use Vertex AI Model Monitoring for drift detection and you want to minimize the cost. What should you do?

Answer options

• A. Use the features for monitoring. Set a monitoring-frequency value that is higher than the default.
• B. Use the features for monitoring. Set a prediction-sampling-rate value that is closer to 1 than 0.
• C. Use the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.
• D. Use the features and the feature attributions for monitoring. Set a prediction-sampling-rate value that is closer to 0 than 1.

Correct answer: D

Explanation

The correct answer is D because using both features and feature attributions for monitoring provides a comprehensive view of model performance, while setting a prediction-sampling-rate closer to 0 reduces costs by limiting the number of predictions processed for monitoring. Options A and B do not effectively minimize costs, and option C may not provide sufficient monitoring detail to detect drift.