The correct answer is that XGBoost handles missing values internally by treating them as a separate category and learning the best split direction. This works through XGBoost’s sparsity-aware algorithm, which, during training, assigns missing values to whichever side of a split reduces the loss function most effectively, effectively learning an optimal default direction for each node. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding that XGBoost does not require external imputation or row deletion—common traps include selecting “impute with mean” or “drop rows,” which are not internal methods. A key memory tip is to remember “learn the direction, don’t fill the gap”: XGBoost automatically decides whether missing values go left or right at each split, so you never need to preprocess them.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 company is using Amazon SageMaker to train an XGBoost model. The training data contains missing values. Which TWO methods can XGBoost handle missing values internally?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Learn the best direction to go when a value is missing.
XGBoost can handle missing values by learning the best direction to go when a value is missing (sparsity-aware algorithm). You don't need to impute or drop. However, the question asks 'which TWO methods can XGBoost handle missing values internally?' The correct answer is that XGBoost can treat missing values as a separate category and learn the optimal split direction. Also, you can set a default direction. But the options: A (impute with mean) is not internal; B (drop rows) is not; C (treat missing as a separate category) is correct; D (learn best split direction) is correct; E (use surrogate splits) is not XGBoost default. So C and D.
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 surrogate splits to handle missing values.
Why it's wrong here
Surrogate splits are used in tree algorithms like CART, not default in XGBoost.
✗
Drop rows with missing values.
Why it's wrong here
Dropping rows is not an internal XGBoost method.
✓
Learn the best direction to go when a value is missing.
Why this is correct
XGBoost uses a sparsity-aware algorithm that learns the optimal split direction for missing values.
Related concept
Read the scenario before looking for a memorised answer.
✓
Treat missing values as a separate category.
Why this is correct
XGBoost can treat missing values as a separate category and learn splits accordingly.
Related concept
Read the scenario before looking for a memorised answer.
✗
Impute missing values with the mean of the feature.
Why it's wrong here
Imputation is not an internal XGBoost method; it requires preprocessing.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Learn the best direction to go when a value is missing. — XGBoost can handle missing values by learning the best direction to go when a value is missing (sparsity-aware algorithm). You don't need to impute or drop. However, the question asks 'which TWO methods can XGBoost handle missing values internally?' The correct answer is that XGBoost can treat missing values as a separate category and learn the optimal split direction. Also, you can set a default direction. But the options: A (impute with mean) is not internal; B (drop rows) is not; C (treat missing as a separate category) is correct; D (learn best split direction) is correct; E (use surrogate splits) is not XGBoost default. So C and D.
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 →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.