CompTIA DataX (DY0-001) — Question 79

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.)

Answer options

• A. Normalization
• B. One-hot encoding
• C. Linearization
• D. Label encoding
• E. Scaling
• F. Pivoting

Correct answer: B, D

Explanation

One-hot encoding (B) creates binary columns for each category, allowing the model to interpret categorical data effectively. Label encoding (D) assigns a unique integer to each category, which is also useful for categorical variables. Normalization (A), Linearization (C), Scaling (E), and Pivoting (F) do not specifically address the transformation of categorical features for model building.