Google Cloud Associate Data Practitioner — Question 52

You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

Answer options

• A. Use BigQuery ML to create a logistic regression model for purchase prediction.
• B. Use Vertex AI Workbench to develop a custom model for purchase prediction.
• C. Use Colab Enterprise to develop a custom model for purchase prediction.
• D. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

Correct answer: A

Explanation

The correct answer is A because BigQuery ML allows users to build machine learning models directly within BigQuery using SQL, making it accessible with minimal engineering resources and ML expertise. Option B and C involve developing custom models which require more ML knowledge and engineering effort, while option D would entail additional steps like exporting data and using AutoML, which may not be necessary given the capabilities of BigQuery ML.