Quick Answer
The answer is mean imputation. For a numerical feature that follows a normal distribution and contains no outliers, the mean is the optimal measure of central tendency because it minimizes the sum of squared errors and preserves the feature’s expected value, making it the most appropriate choice for imputation. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of how imputation methods interact with data distributions—specifically, that mean imputation is only suitable when the data is normally distributed and free of extreme values, as outliers would skew the mean and introduce bias. A common trap is to default to median imputation, which is robust to outliers but unnecessary here, or to drop rows, which reduces sample size and can degrade model performance. Memory tip: “Mean for the bell, median for the skew”—if your feature looks like a bell curve, use the mean.
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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.
A data scientist is preparing a dataset for a linear regression model. The dataset has a few missing values in a numerical feature with a normal distribution and no outliers. Which imputation method is most appropriate?
Impute with mode
Why wrong: Mode is for categorical variables, not numerical.
Impute with mean
Mean is appropriate for normally distributed numerical data without outliers.
Impute with median
Why wrong: Median is robust to outliers, but not necessary here and may introduce bias.
Drop rows with missing values
Why wrong: Dropping rows reduces dataset size and may bias the model.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Impute with mean
Option B is correct because mean imputation is suitable for normally distributed data without outliers. Option A (drop rows) reduces sample size. Option C (median) is robust to outliers but not needed. Option D (mode) is for categorical data.
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.
Impute with mode
Why it's wrong here
Mode is for categorical variables, not numerical.
Impute with mean
Why this is correct
Mean is appropriate for normally distributed numerical data without outliers.
Related concept
Read the scenario before looking for a memorised answer.
Impute with median
Why it's wrong here
Median is robust to outliers, but not necessary here and may introduce bias.
Drop rows with missing values
Why it's wrong here
Dropping rows reduces dataset size and may bias the model.
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
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Impute with mean — Option B is correct because mean imputation is suitable for normally distributed data without outliers. Option A (drop rows) reduces sample size. Option C (median) is robust to outliers but not needed. Option D (mode) is for categorical data.
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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