Google Cloud Professional Machine Learning Engineer — Question 83

You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer’s loan request has been rejected by your model, and the bank’s risks department is asking you to provide the reasons that contributed to the model’s decision. What should you do?

Answer options

• A. Use local feature importance from the predictions.
• B. Use the correlation with target values in the data summary page.
• C. Use the feature importance percentages in the model evaluation page.
• D. Vary features independently to identify the threshold per feature that changes the classification.

Correct answer: A

Explanation

The correct answer is A because local feature importance provides insights into which specific features influenced the model's decision for that individual customer. The other options either provide general insights or aggregate information that does not focus on the individual prediction, making them less suitable for this specific inquiry.