You work for a company that sells corporate electronic products to thousands of businesses worldwide. The company keeps historical customer data in BigQuery.
You need to build a model that forecasts customer lifetime value for the next three years. You want the simplest way to build the model while retaining 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. Show Answer Answer Explanation A Vertex AI Workbench notebook supplies an interactive environment for exploratory data analysis and visualizations. BigQuery IPython magics allow BigQuery SQL to run from that notebook, so feature data can remain in BigQuery while CREATE MODEL, ML.EVALUATE, and ML.PREDICT create, evaluate, and use the model. This avoids an unnecessary CSV export/import step and keeps the workflow in one environment.
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