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Answer: Run the CREATE MODEL statement from the BigQuery console to create an AutoML model. Validate the results by using the ML.EVALUATE and ML.PREDICT statements.
The question emphasizes using the 'simplest approach' while requiring access to visualization tools. Option B directly uses BigQuery's built-in AutoML capabilities via the CREATE MODEL statement, which automates feature engineering, model selection, and training without manual data movement or additional tools. This minimizes complexity and effort. BigQuery integrates with visualization tools like Looker, meeting the visualization requirement. Community discussion supports B as the simplest option (54% consensus, with comments highlighting AutoML's automation and reduced effort). Other options (A, C, D) involve unnecessary steps like creating new BigQuery tables or using Vertex AI Workbench for EDA, which add complexity without clear benefits for the 'simplest approach' goal.
Author: LeetQuiz Editorial Team
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You need to build a model to predict customer lifetime value for the next three years using your company's historical customer data stored in BigQuery. You want to use the simplest approach and have access to visualization tools. What should you do?
A
Create a Vertex AI Workbench notebook to perform exploratory data analysis. Use IPython magics to create a new BigQuery table with input features. Use the BigQuery console to run the CREATE MODEL statement. Validate the results by using the ML.EVALUATE and ML.PREDICT statements.
B
Run the CREATE MODEL statement from the BigQuery console to create an AutoML model. Validate the results by using the ML.EVALUATE and ML.PREDICT statements.
C
Create a Vertex AI Workbench notebook to perform exploratory data analysis and create input features. Save the features as a CSV file in Cloud Storage. Import the CSV file as a new BigQuery table. Use the BigQuery console to run the CREATE MODEL statement. Validate the results by using the ML.EVALUATE and ML.PREDICT statements.
D
Create a Vertex AI Workbench notebook to perform exploratory data analysis. Use IPython magics to create a new BigQuery table with input features, create the model, and validate the results by using the CREATE MODEL, ML.EVALUATE, and ML.PREDICT statements.