Google Professional Machine Learning Engineer
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You are developing a machine learning model to predict product sales using historical data stored in BigQuery. The data includes features like date, store location, product category, and promotion details. To maximize prediction accuracy, what is the most effective combination of a BigQuery ML model type and feature engineering strategy?
A
Use a linear regression model. Perform one-hot encoding on categorical features, and create additional features based on the date, such as day of the week or month.
B
Use a boosted tree model. Perform label encoding on categorical features, and transform the date column into numeric values.
C
Use an autoencoder model. Perform label encoding on categorical features, and normalize the date column.
D
Use a matrix factorization model. Perform one-hot encoding on categorical features, and create interaction features between the store location and product category variables.