Google Cloud Professional Machine Learning Engineer — Question 72

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

Answer options

• A. Train local surrogate models to explain individual predictions.
• B. Configure sampled Shapley explanations on Vertex Explainable AI.
• C. Configure integrated gradients explanations on Vertex Explainable AI.
• D. Measure the effect of each feature as the weight of the feature multiplied by the feature value.

Correct answer: B

Explanation

The correct answer is B because sampled Shapley explanations provide a clear method for interpreting model predictions by assigning importance to each feature based on its contribution to the prediction. Option A is incorrect as local surrogate models do not directly provide the aggregated insights needed for comparison. Option C, while useful for understanding feature influence, does not offer the same level of clarity for individual predictions as Shapley values. Option D focuses on feature weighting but does not effectively explain the prediction discrepancy.