The correct answer is multiple imputation and using algorithms that handle missing values internally, such as XGBoost. Multiple imputation works by creating several complete datasets through iterative modeling of the missing entries, then pooling the results to account for uncertainty, which preserves statistical power and reduces bias compared to simpler methods. Algorithms like XGBoost are effective because they learn split directions that accommodate missing data during training, making them robust without requiring preprocessing. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how to avoid common pitfalls like mean imputation, which can distort feature distributions, or listwise deletion, which shrinks your sample size and may introduce selection bias. A frequent trap is assuming that dropping rows or columns with missing values is always safe, but the exam emphasizes preserving information when possible. Remember the memory tip: “Impute with multiple models, or let the tree handle the holes.”
MLS-C01 Exploratory Data Analysis Practice Question
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 of the following are appropriate methods for handling missing data in a dataset?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Multiple imputation
Multiple imputation and using algorithms that handle missing values (e.g., XGBoost) are valid. Listwise deletion reduces sample size. Mean imputation may bias distributions. Dropping features with many missing values may lose information.
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.
✗
Dropping features with more than 50% missing values
Why it's wrong here
Dropping features may discard useful information; imputation is often preferred.
✗
Mean imputation for all features
Why it's wrong here
Mean imputation can distort relationships and reduce variance.
✓
Multiple imputation
Why this is correct
Multiple imputation accounts for uncertainty by creating multiple datasets.
Related concept
Read the scenario before looking for a memorised answer.
✓
Using algorithms that handle missing values internally (e.g., XGBoost)
Why this is correct
Some tree-based algorithms can handle missing values by learning split directions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Listwise deletion (removing rows with missing values)
Why it's wrong here
Listwise deletion can reduce sample size significantly and introduce bias.
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.
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: Multiple imputation — Multiple imputation and using algorithms that handle missing values (e.g., XGBoost) are valid. Listwise deletion reduces sample size. Mean imputation may bias distributions. Dropping features with many missing values may lose information.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO of the following are appropriate techniques for handling missing data during exploratory data analysis? (Select TWO.)
medium
A.Ignore missing values and proceed with modeling
B.Replace missing values with -1 to indicate missing
✓ C.Impute missing values using mean or median for numerical features
✓ D.Visualize the missing data pattern using heatmaps or bar charts
E.Delete all rows with any missing values
Why C: Options A and C are correct. Visualizing missing data patterns (A) helps understand the missing mechanism. Using imputation methods like mean/median (C) is common during EDA. Option B is wrong because deleting all rows with missing values may discard too much data. Option D is wrong because ignoring missing values can lead to errors. Option E is wrong because replacing with -1 can distort data.
Last reviewed: Jun 20, 2026
Question Discussion
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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.
Question Discussion
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