Question 885 of 1,672
Handling Missing Categorical Values
Which TWO approaches are valid for handling missing categorical values in a dataset before training a machine learning model?
Quick Answer
The answer is to treat missing values as a separate category and impute with the mode. These two approaches are valid because categorical data lacks a meaningful mean or median, making the mode—the most frequent category—the only appropriate measure of central tendency for imputation. Treating missingness as its own distinct category preserves the informational signal that a value was absent, which can be predictive in itself, while mode imputation maintains the original distribution of categories without introducing bias. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of preprocessing pipelines and the limitations of scikit-learn’s SimpleImputer, where strategy='most_frequent' is the correct choice for categorical features. A common trap is attempting to use mean imputation on categorical data, which produces nonsensical values. Remember the mnemonic: “For categories, mode is the code; for missing, make a new abode.”
⚠ Common exam trap
AWS often tests the distinction between numerical and categorical imputation methods, trapping candidates who apply mean or median imputation to categorical features without recognizing that these statistics are invalid for non-numeric 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
✓
Impute missing values with the mode of the column
The mode (most frequent value) is the only valid measure of central tendency for categorical data, as it identifies the most common category. Imputing with the mode preserves the distribution of categories and is a standard technique for handling missing categorical values in preprocessing pipelines like scikit-learn's SimpleImputer with strategy='most_frequent'.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all rows with missing values
Why it's wrong here
Deleting rows may cause data loss.
- ✓
Impute missing values with the mode of the column
Why this is correct
Mode is appropriate for categorical data.
- ✗
Impute missing values with the median of the column
Why it's wrong here
Median is for numerical data.
- ✗
Impute missing values with the mean of the column
Why it's wrong here
Mean is for numerical, not categorical.
- ✓
Treat missing values as a separate category
Why this is correct
Encoding missingness can be informative.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO of the following are valid approaches to handle missing values in a dataset for a machine learning model?
medium- A.Use a neural network to predict missing values
- ✓ B.Impute missing values with the mean of the column
- ✓ C.Remove rows with missing values
- D.Standardize the features to handle missing values
- E.Apply one-hot encoding to convert missing values
Why B: Removing rows with missing values is a valid approach (listwise deletion). Imputing with the mean is also valid. Using a neural network to predict missing values is possible but not standard. Standardization does not handle missing values. One-hot encoding is for categorical variables.
Last reviewed: Jun 30, 2026
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