easyMultiple Choice
MLA-C01 Practice Question: Which technique is commonly used to handle…
Which technique is commonly used to handle missing values in a categorical feature?
⚠ Common exam trap
The MLA-C01 exam often tests the distinction between data preprocessing techniques (imputation) and feature engineering techniques (encoding, scaling), leading candidates to mistakenly select one-hot encoding as a missing-value handler because it is commonly associated with categorical data.
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
✓
Mode imputation
Mode imputation is the standard technique for handling missing values in categorical features because it replaces missing entries with the most frequent category, preserving the feature's distribution without introducing artificial values. Unlike numerical imputation methods, mode imputation respects the non-numeric nature of categorical data and maintains the integrity of the original categories.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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One-hot encoding
Why it's wrong here
One-hot encoding converts categories into binary indicator columns; it does not fill absent values, and a missing category simply yields all-zero rows. It is tempting because it is the standard categorical preprocessing step, but it addresses encoding rather than imputation, which is what handling missing values requires.
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Mean imputation
Why it's wrong here
Mean imputation averages numeric values, which is undefined for categorical labels, so it cannot supply a missing category. It is tempting because imputation is the right family of technique, but the mean applies to continuous data; categorical gaps need mode or constant imputation instead.
- ✓
Mode imputation
Why this is correct
Mode imputation replaces missing entries with the most frequent category, preserving the feature's existing distribution rather than inventing values. Unlike mean or median imputation, which require numeric data, it operates directly on categorical variables, satisfying the stem's constraint that the feature is categorical.
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Standard scaling
Why it's wrong here
Standard scaling centres and scales numeric values, so it cannot operate on categories and leaves missing entries untouched. It is tempting because scaling is a preprocessing step, but it applies to continuous features; for categorical gaps the correct approach is imputation such as a constant or mode value.
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