Google Cloud Professional Machine Learning Engineer — Question 154

You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company’s historical data as your training set, you created a TensorFlow model and deployed it to Vertex AI. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

Answer options

• A. Stream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
• B. Use Vertex Explainable AI. Submit each prediction request with the explain' keyword to retrieve feature attributions using the sampled Shapley method.
• C. Use Vertex AI Workbench user-managed notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
• D. Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.

Correct answer: B

Explanation

The correct answer is B because Vertex Explainable AI provides a method to obtain feature attributions, allowing you to see the impact of each attribute on the prediction through the Shapley method. Option A focuses on correlation but does not directly provide predictive power for individual predictions. Option C suggests using Lasso regression, which may not yield immediate insights on feature importance for each prediction. Option D analyzes model performance changes but does not specifically quantify individual feature contributions.