Quick Answer
The answer is multiple imputation by chained equations (MICE). This method is most appropriate for imputation for non-random missing data because it models each feature with missing values as a function of the other features, iteratively using regression or other predictive models to fill in plausible values while preserving the underlying correlations between variables. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that when missingness is correlated with another feature—a classic case of non-random missing data—simple techniques like mean or median imputation introduce bias, and deletion wastes valuable data. A common trap is defaulting to mean imputation for speed, but the exam expects you to recognize that MICE leverages the relationships among features to produce less biased estimates. Memory tip: MICE stands for “Multiple Imputation by Chained Equations”—think of it as a team of mice working together, each mouse (equation) filling in one feature’s gaps based on the others, so the missing data doesn’t stay random.
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 analyzing a dataset with 100,000 observations and 50 features. The scientist uses a Jupyter notebook on Amazon SageMaker. During EDA, the scientist runs a command to check for missing values and notices that 20% of the data in one feature is missing. The missing values are not random; they are correlated with another feature. Which imputation method is MOST appropriate?
Median imputation
Why wrong: Does not account for correlation.
Listwise deletion (remove rows with missing values)
Why wrong: Loses data and may bias results.
Mean imputation
Why wrong: Does not account for correlation.
Multiple imputation by chained equations (MICE)
Models missing values using other features.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Multiple imputation by chained equations (MICE)
Option D is correct because MICE uses multiple imputation based on other features, accounting for correlations. Option A is wrong because mean imputation ignores correlation. Option B is wrong because median imputation also ignores correlation. Option C is wrong because removing rows loses data and may introduce bias.
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.
Median imputation
Why it's wrong here
Does not account for correlation.
Listwise deletion (remove rows with missing values)
Why it's wrong here
Loses data and may bias results.
Mean imputation
Why it's wrong here
Does not account for correlation.
Multiple imputation by chained equations (MICE)
Why this is correct
Models missing values using other features.
Related concept
Read the scenario before looking for a memorised answer.
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 MLS-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
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Multiple imputation by chained equations (MICE) — Option D is correct because MICE uses multiple imputation based on other features, accounting for correlations. Option A is wrong because mean imputation ignores correlation. Option B is wrong because median imputation also ignores correlation. Option C is wrong because removing rows loses data and may introduce bias.
Identify which MLS-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 20, 2026
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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