AI Associate Data for AI Practice Question
Which data transformation is most appropriate for converting categorical variables into numerical format for a machine learning model?
⚠ Common exam trap
Salesforce often tests the distinction between data preprocessing techniques (normalization, standardization) and encoding methods, trapping candidates who confuse scaling with categorical conversion.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
One-hot encoding.
One-hot encoding is the correct transformation because it converts categorical variables into a binary vector representation, where each category becomes a separate column with a 1 or 0. This allows machine learning models to interpret categorical data without implying any ordinal relationship, which is essential for algorithms that rely on numerical distances or linear algebra.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalization.
Why it's wrong here
Normalization scales numerical values to a range.
- ✓
One-hot encoding.
Why this is correct
One-hot encoding creates binary columns for each category, making them usable in models.
- ✗
Principal component analysis.
Why it's wrong here
PCA is a dimensionality reduction technique, not a transformation for categorical data.
- ✗
Standardization.
Why it's wrong here
Standardization is for numerical features, not categorical.
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