Question

A data scientist is building a logistic regression model in BigQuery ML to predict customer churn. The training table includes a numeric 'monthly_spend' column with a wide range of values and a 'subscription_tier' column containing text values like 'Basic', 'Pro', and 'Enterprise'. They want the same preprocessing (standardizing the numeric column and one-hot encoding the categorical column) to be automatically applied during both training and prediction without maintaining separate SQL logic. What is the most appropriate approach?

Accepted Answer

Use the TRANSFORM clause in the CREATE MODEL statement with ML.STANDARD_SCALER for monthly_spend, letting BigQuery ML handle categorical encoding automatically.

Answer

Manually create a preprocessed view that scales and one-hot encodes the columns, then reference it in both training and prediction queries.

Answer

Export the data to Vertex AI, apply preprocessing in a custom container, and import the model back into BigQuery ML.

Answer

Cast monthly_spend to a STRING and concatenate it with subscription_tier so BigQuery ML treats them as a single categorical feature.