Quick Answer
The correct answer is to impute missing values with the mean or median of the feature, as this preserves dataset size while maintaining central tendency for numerical data. This technique is appropriate because it avoids the data loss caused by dropping rows and prevents the bias introduced by arbitrary values like zero, which can distort distributions and model training. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of data preprocessing trade-offs, often appearing in scenario-based questions where you must choose between imputation, deletion, or feature removal. A common trap is selecting "drop all rows with missing values" without considering sample size, or "fill with zeros" which assumes missing equals zero—a dangerous assumption for non-binary features. For memory, remember the "MID" rule: Mean/Median Imputation is safe for numerical data, but avoid Drops and Zeros unless the context explicitly supports them.
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 actions are appropriate when handling missing data in a dataset for machine learning? (Select TWO.)
Use a machine learning model to predict missing values based on other features
Why D is correct
Drop all rows that contain any missing value
Why wrong: Why B is wrong
Impute missing values with the mean or median of the feature
Why A is correct
Remove the feature entirely if it contains missing values
Why wrong: Why E is wrong
Fill missing values with zero
Why wrong: Why C is wrong
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a machine learning model to predict missing values based on other features
Options A and D are correct. Imputing with mean/median is a common technique, and using a model to predict missing values is also valid. Option B is wrong because dropping all rows with missing values can discard too much data. Option C is wrong because filling with zeros may not be appropriate for all features. Option E is wrong because removing the feature entirely may lose important information.
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.
Use a machine learning model to predict missing values based on other features
Why this is correct
Why D is correct
Related concept
Read the scenario before looking for a memorised answer.
Drop all rows that contain any missing value
Why it's wrong here
Why B is wrong
Impute missing values with the mean or median of the feature
Why this is correct
Why A is correct
Related concept
Read the scenario before looking for a memorised answer.
Remove the feature entirely if it contains missing values
Why it's wrong here
Why E is wrong
Fill missing values with zero
Why it's wrong here
Why C is wrong
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.
Exploratory Data Analysis — study guide chapter
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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: Use a machine learning model to predict missing values based on other features — Options A and D are correct. Imputing with mean/median is a common technique, and using a model to predict missing values is also valid. Option B is wrong because dropping all rows with missing values can discard too much data. Option C is wrong because filling with zeros may not be appropriate for all features. Option E is wrong because removing the feature entirely may lose important information.
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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