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Answer: Access BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create an AutoML regression model.
The question emphasizes using 'the simplest approach' to build a model predicting customer lifetime value (CLTV) over three years, with data already in BigQuery. Option B is optimal because: 1) BigQuery Studio allows direct SQL-based model creation without additional environment setup, minimizing complexity compared to Vertex AI Workbench (options A and C), which requires notebook configuration and Python code. 2) AutoML regression (B) is more suitable for CLTV prediction than ARIMA (D), as CLTV relies on diverse customer features (e.g., purchase history, demographics) rather than pure time-series patterns, making AutoML a general and accurate choice. The community discussion (100% consensus for B, with upvoted comments) reinforces that BigQuery Studio with AutoML regression balances simplicity and effectiveness, avoiding unnecessary overhead from Vertex AI or the specialized but less fitting ARIMA model.
Author: LeetQuiz Editorial Team
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You work for a company that sells corporate electronics to thousands of businesses globally. Your company stores historical customer data in BigQuery. You need to build a model to predict customer lifetime value for the next three years and want to use the simplest approach. What should you do?
A
Create a Vertex AI Workbench notebook. Use IPython magic to run the CREATE MODEL statement to create an ARIMA model.
B
Access BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create an AutoML regression model.
C
Create a Vertex AI Workbench notebook. Use IPython magic to run the CREATE MODEL statement to create an AutoML regression model.
D
Access BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create an ARIMA model.