Google Cloud Professional Machine Learning Engineer — Question 253

You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

Answer options

• A. Build a random forest regression model in a Vertex AI Workbench notebook instance. Configure the model to generate feature importances after the model is trained.
• B. Build an AutoML tabular regression model. Configure the model to generate explanations when it makes predictions.
• C. Build a custom TensorFlow neural network by using Vertex AI custom training. Configure the model to generate explanations when it makes predictions.
• D. Build a random forest classification model in a Vertex AI Workbench notebook instance. Configure the model to generate feature importances after the model is trained.

Correct answer: D

Explanation

The correct answer is D because a random forest classification model can effectively predict customer churn and provide interpretable feature importances. Options A and B utilize regression models, which are unsuitable for classification tasks like churn prediction. Option C, while capable of generating explanations, does not guarantee the same level of interpretability as a random forest classification model in this context.