- A
Use a model that handles missing values natively, such as XGBoost
Correct: Some algorithms can handle missing values without imputation.
- B
Replace missing values with a constant like -999
Why wrong: Incorrect: Constant replacement can distort distributions.
- C
Impute missing values with the median
Correct: Imputation retains rows and fills values.
- D
Drop columns with high missing percentage
Why wrong: Incorrect: Dropping columns loses features.
- E
Drop rows with any missing value
Why wrong: Incorrect: Dropping rows reduces sample size.
Question 185 of 1,755
Retain Data When Handling Missing Values: XGBoost and Imputation
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.
A data scientist is performing exploratory data analysis on a dataset with missing values. Which TWO approaches are appropriate for handling missing data in a way that retains as much data as possible?
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
✓
Use a model that handles missing values natively, such as XGBoost
Correct options: A and C. Option A (use a model that handles missing values natively, such as XGBoost) retains all rows and avoids deletion. Option C (impute missing values with the median) fills missing values without reducing data. Option B is wrong because dropping rows reduces data. Option D is wrong because dropping columns loses features. Option E is wrong because replacing with a constant may bias.
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.
- ✓
Use a model that handles missing values natively, such as XGBoost
Why this is correct
Correct: Some algorithms can handle missing values without imputation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Replace missing values with a constant like -999
Why it's wrong here
Incorrect: Constant replacement can distort distributions.
- ✓
Impute missing values with the median
Why this is correct
Correct: Imputation retains rows and fills values.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Drop columns with high missing percentage
Why it's wrong here
Incorrect: Dropping columns loses features.
- ✗
Drop rows with any missing value
Why it's wrong here
Incorrect: Dropping rows reduces sample size.
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 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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Exploratory Data Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a model that handles missing values natively, such as XGBoost — Correct options: A and C. Option A (use a model that handles missing values natively, such as XGBoost) retains all rows and avoids deletion. Option C (impute missing values with the median) fills missing values without reducing data. Option B is wrong because dropping rows reduces data. Option D is wrong because dropping columns loses features. Option E is wrong because replacing with a constant may bias.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
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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