The answer is to leave missing values as-is, because XGBoost handles them natively during training. For each split, XGBoost learns the optimal direction—left or right child node—for missing values by evaluating which assignment minimizes the loss function, making explicit imputation unnecessary. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of XGBoost’s built-in sparsity-aware algorithm, which treats missing values as a separate category rather than requiring preprocessing. A common trap is assuming you must impute with mean, median, or zero, but SageMaker’s XGBoost implementation preserves this native behavior. Remember the mnemonic: “Missing? Let XGBoost decide the split side.”
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 data scientist is using Amazon SageMaker to train an XGBoost model for a regression problem. The training data contains missing values in some features. Which approach should the data scientist use to handle missing values in XGBoost?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Leave missing values as-is; XGBoost handles them natively
XGBoost has a built-in mechanism to handle missing values natively by learning the best direction to split on missing values during training. For each split, XGBoost assigns missing values to the left or right child node based on which direction minimizes the loss function, making explicit imputation unnecessary for this algorithm.
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 K-nearest neighbors imputation
Why it's wrong here
KNN imputation is computationally expensive and not needed.
✓
Leave missing values as-is; XGBoost handles them natively
Why this is correct
XGBoost can handle missing values by learning the optimal direction to split.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove all rows with missing values
Why it's wrong here
Removing rows reduces the dataset size and may introduce bias.
✗
Impute missing values with the mean of the column
Why it's wrong here
Imputation is possible but XGBoost can handle missing values natively, making it unnecessary.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often default to common imputation techniques (like mean imputation or row removal) without recognizing that XGBoost has a built-in, algorithm-specific method for handling missing values, which is a key differentiator tested in the MLS-C01 exam.
Detailed technical explanation
How to think about this question
XGBoost's sparsity-aware algorithm treats missing values as a separate category and learns the optimal split direction (left or right) by evaluating the loss reduction for each assignment during tree construction. This is implemented via a 'missing' direction in the tree node, and the algorithm uses a default direction learned from training data, which is applied during inference. In real-world scenarios with high-dimensional sparse data, this approach avoids the need for imputation and can improve generalization by leveraging missingness as a signal.
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.
TExam Day Tips
→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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Leave missing values as-is; XGBoost handles them natively — XGBoost has a built-in mechanism to handle missing values natively by learning the best direction to split on missing values during training. For each split, XGBoost assigns missing values to the left or right child node based on which direction minimizes the loss function, making explicit imputation unnecessary for this algorithm.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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. A data scientist is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. The dataset contains missing values. What is the default behavior of SageMaker XGBoost regarding missing values?
easy
A.It raises an error and stops training
B.It imputes missing values with the column mean
C.It removes rows with missing values
✓ D.It automatically learns the best direction (left or right) for missing values during training
Why D: Option A is correct because XGBoost treats missing values as a separate category and learns the best direction to handle them (by default). Option B (mean imputation) is not default; XGBoost handles missingness internally. Option C (removing rows) is not default. Option D (fail) is not default.
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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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