During exploratory data analysis several categorical features contain missing values. How can the ML engineer use SageMaker to handle these missing categorical values?

SageMaker Data Wrangler is a data preparation service that offers various transformations, including imputation for missing values. For categorical features, the most appropriate imputation strategy is to replace missing values with the mode (the most frequent category). Using the mean value is incorrect for categorical data, as the mean is a measure of central tendency for numerical data and has no meaningful interpretation for categories. SageMaker Clarify is primarily used for bias detection and explainability of machine learning models, not for data preparation or imputation. Therefore, using SageMaker Data Wrangler to impute categorical features with the mode value is the correct and most effective approach.