MLS-C01 Exploratory Data Analysis Practice Question

During exploratory data analysis, a machine learning engineer finds that a dataset has a significant number of missing values in a categorical feature with 10 levels. Which approach should they take to handle these missing values before modeling?

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

✓

Create a new category labeled 'Missing' for missing values.

Creating a separate 'Missing' category preserves the missingness pattern and avoids data loss or bias from imputation for categorical features. Option A is incorrect because mean imputation is for numerical features, not categorical. Option C is incorrect because dropping all rows with missing values may discard valuable data and reduce sample size. Option D is incorrect because mode imputation may introduce bias if missingness is not random.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Impute missing values with the mean of the feature.
Why it's wrong here
Mean is not meaningful for categorical data.
✓
Create a new category labeled 'Missing' for missing values.
Why this is correct
Preserves the missingness pattern and avoids bias.
✗
Drop all rows with missing values.
Why it's wrong here
May discard significant data.
✗
Impute missing values with the mode of the feature.
Why it's wrong here
Mode imputation can introduce bias if missingness is non-random.

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Last reviewed: Jun 20, 2026

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