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A data scientist is analyzing a data set with categorical features and would like to make those features more useful when building a model. Which of the following data transformation techniques should the data scientist use? (Choose two.)
Correct Answer: B,D
# Categorical variables must be transformed into numerical form for most machine learning models. Two standard approaches: * One-hot encoding: Converts each category into a separate binary column (useful for nominal variables). * Label encoding: Converts categories into integers (useful for ordinal or tree-based models). Why other options are incorrect: * A & E: Normalization and scaling are used for continuous variables, not categorical. * C: Linearization refers to transforming relationships, not categorical conversion. * F: Pivoting rearranges data structure but doesn't encode categories. Official References: * CompTIA DataX (DY0-001) Study Guide - Section 3.3:"Label encoding and one-hot encoding are common transformations applied to categorical variables to enable model compatibility." -
