Google Cloud Professional Machine Learning Engineer — Question 310

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. Use Vertex AI to evaluate the model performance.
• B. Create a logistic regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Use ML.ARIMA_EVALUATE function to evaluate the model performance.
• 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: D

Explanation

The correct answer is D because logistic regression is suitable for predicting binary outcomes like customer churn. The ML.CONFUSION_MATRIX function provides a detailed evaluation of the model's performance in terms of true positives, false positives, and accuracy. The other options incorrectly suggest linear regression for a classification problem or use inappropriate evaluation methods for the logistic regression model.