- A
Median imputation
Median imputation is robust to skewness and outliers, preserving the central tendency without distorting the distribution shape.
- B
Mode imputation
Why wrong: Mode imputation is used for categorical data, not numeric features.
- C
Mean imputation
Why wrong: Mean imputation is sensitive to outliers and skewness, potentially biasing the distribution toward the tail.
- D
Drop rows with missing values
Why wrong: Dropping rows reduces sample size and may introduce bias if missingness is not random.
Question 419 of 1,000
hardMultiple ChoiceObjective-mapped
Median Imputation — Handling Missing Values in Skewed Distributions | AWS Certified Machine Learning Engineer Associate Explained
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
During data quality assessment, a data scientist discovers that a numeric feature has many missing values. The feature is expected to have a skewed distribution. The scientist wants to impute missing values in a way that preserves the distribution shape and does not introduce bias toward the center. Which imputation strategy is MOST appropriate?
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
✓
Median imputation
Median imputation is robust to skewness and does not pull imputed values toward the tail, preserving the overall distribution shape. Mean imputation can be affected by outliers and skewness. Mode imputation is for categorical data, and dropping rows may remove useful data.
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.
- ✓
Median imputation
Why this is correct
Median imputation is robust to skewness and outliers, preserving the central tendency without distorting the distribution shape.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Mode imputation
Why it's wrong here
Mode imputation is used for categorical data, not numeric features.
- ✗
Mean imputation
Why it's wrong here
Mean imputation is sensitive to outliers and skewness, potentially biasing the distribution toward the tail.
- ✗
Drop rows with missing values
Why it's wrong here
Dropping rows reduces sample size and may introduce bias if missingness is not random.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 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
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Median imputation — Median imputation is robust to skewness and does not pull imputed values toward the tail, preserving the overall distribution shape. Mean imputation can be affected by outliers and skewness. Mode imputation is for categorical data, and dropping rows may remove useful data.
What should I do if I get this MLA-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 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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