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A data scientist is working with a feature set with the following schema: The customer_id column is the primary key in the feature set. Each of the columns in the feature set has missing values. They want to replace the missing values by imputing a common value for each feature. Which of the following lists all of the columns in the feature set that need to be imputed using the most common value of the column?
Correct Answer: B
For the feature set schema provided, the columns that need to be imputed using the most common value (mode) are typically the categorical columns. In this case, loyalty_tier is the only categorical column that should be imputed using the most common value. customer_id is a unique identifier and should not be imputed, while spend and units are numerical columns that should typically be imputed using the mean or median values, not the mode. Reference: Databricks documentation on missing value imputation: Handling Missing Data If you need any further clarification or additional questions answered, please let me know!
