Google Cloud Professional Machine Learning Engineer — Question 232

You work for a large retailer, and you need to build a model to predict customer chum. The company has a dataset of historical customer data, including customer demographics purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

Answer options

• A. Create a linear regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .
• B. Create a logistic regression model in BigQuery ML and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .
• C. Create a linear regression model in BigQuery ML. Use the ML.EVALUATE function to evaluate the model performance.
• D. Create a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to evaluate the model performance.

Correct answer: B

Explanation

The correct answer is B because customer churn is a classification problem, making logistic regression the appropriate model type to predict binary outcomes. Options A and C incorrectly suggest linear regression, which is not suitable for predicting categories like churn. Option D, while using logistic regression, does not utilize the most effective evaluation method for assessing model performance in this context.