Handling null values in numeric features requires careful consideration to maintain data integrity. The correct answer, using imputation techniques like ALS (C), is effective for estimating missing values based on other data features. Replacing nulls with the mean or median (E) is a straightforward approach but may be skewed by outliers. Introducing a special category for nulls (D) can be meaningful if missingness carries information. Dropping records (A) risks data loss and bias, while using the mode (B) or random samples (F) may not always preserve data distribution. The choice depends on data characteristics and analysis goals.