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You work for a company that specializes in selling corporate electronic products to thousands of businesses worldwide, including laptops, servers, and networking equipment. Your company stores a vast amount of historical customer data in BigQuery, detailing purchase histories, customer interactions, and support tickets. You have been tasked with building a machine learning model that predicts customer lifetime value (CLV) over the next three years to assist in customer retention efforts and targeted marketing campaigns. Given that you aim to adopt the simplest approach to build this model and require access to visualization tools for better data insight, which approach should you take?
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.