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.”
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO approaches are valid for handling missing categorical values in a dataset before training a machine learning model?
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
Option B is correct because 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'.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, imputing with the mode works by calculating the frequency distribution of each category and selecting the one with the highest count; in scikit-learn, SimpleImputer with strategy='most_frequent' handles this efficiently. A subtle behavior is that if multiple categories tie for the mode, the first encountered category is used, which can introduce bias in rare cases. In real-world scenarios like customer segmentation, treating missing values as a separate category (option E) is often preferred when the absence of a value itself carries predictive signal, such as a missing 'Marital Status' indicating a non-response pattern.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Impute missing values with the mode of the column — Option B is correct because 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'.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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