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You are developing a linear regression model using BigQuery ML for a dataset with numerous features stored in a view. To enhance model performance and prevent overfitting, you decide to implement regularization. The dataset includes a mix of numeric and categorical features, and you aim to exclude only the label column from the feature set during model training. Given the need for efficiency and the avoidance of overfitting, which SQL clause should you incorporate in your query to dynamically exclude the label column while including all other columns from the view? Choose the best option.
A
Using the UNNEST clause to flatten arrays or structs into rows, assuming the label is stored in a nested structure.
B
Applying the EXCEPT clause to explicitly exclude the label column from the selection, allowing all other columns to be included.
C
Utilizing the LAG window function to reference the label column from a previous row, effectively excluding it from the current row's features.
D
Implementing the ROLLUP clause to generate subtotals and grand totals, indirectly excluding the label column by focusing on aggregated data.
E
Combining both EXCEPT and LAG clauses to ensure the label column is excluded and to leverage historical data for feature engineering.