Quick Answer
The answer is mode imputation, which replaces missing values with the most frequent category in the feature. This technique is standard for handling missing categorical values because it preserves the existing distribution of the data without introducing artificial categories or numerical bias, unlike mean imputation which is reserved for numerical features. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept often appears in questions about data preprocessing pipelines, testing your ability to distinguish between appropriate methods for categorical versus numerical data. A common trap is confusing mode imputation with one-hot encoding, which only creates binary columns and does not address missingness, or with standard scaling, which is a normalization technique for continuous variables. To remember this, think of the word “mode” as the “most common” value—just as you would guess the most popular answer in a multiple-choice question when you are unsure.
This MLA-C01 practice question tests your understanding of ml model development. 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 technique is commonly used to handle missing values in a categorical feature?
One-hot encoding
Why wrong: One-hot encoding transforms categories into binary vectors but does not handle missing values.
Mean imputation
Why wrong: Mean imputation is appropriate for numerical features, not categorical.
Mode imputation
Mode imputation replaces missing categorical values with the most frequent category, a common practice.
Standard scaling
Why wrong: Standard scaling normalizes numerical features, not for missing values.
Answer choices
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 (replacing missing values with the most frequent category) is a standard method for categorical data. Mean imputation is for numerical data, standard scaling is for feature scaling, and one-hot encoding encodes categories without handling missing values.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
One-hot encoding
Why it's wrong here
One-hot encoding transforms categories into binary vectors but does not handle missing values.
Mean imputation
Why it's wrong here
Mean imputation is appropriate for numerical features, not categorical.
Mode imputation
Why this is correct
Mode imputation replaces missing categorical values with the most frequent category, a common practice.
Related concept
Read the scenario before looking for a memorised answer.
Standard scaling
Why it's wrong here
Standard scaling normalizes numerical features, not for missing values.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
The correct answer is: Mode imputation — Mode imputation (replacing missing values with the most frequent category) is a standard method for categorical data. Mean imputation is for numerical data, standard scaling is for feature scaling, and one-hot encoding encodes categories without handling missing values.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 2026
This MLA-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 MLA-C01 exam.
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