Quick Answer
The answer is to choose a CPU instance type for the training job. XGBoost is a CPU-native algorithm that does not require a GPU to train, even though GPU-accelerated versions exist for other frameworks. The error message is misleading because it typically arises when a GPU-only algorithm version is accidentally selected or when the SageMaker built-in container is misconfigured to expect a GPU instance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that built-in XGBoost in SageMaker runs efficiently on CPU instances, and the common trap is assuming a GPU is mandatory for any machine learning task. Remember that XGBoost’s tree-based architecture is optimized for CPU parallelism, not GPU tensor operations. A useful memory tip: “XGBoost doesn’t need a boost from a GPU—CPU is its natural home.”
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 uses Amazon SageMaker to train a classification model. The training job fails with an error indicating that the algorithm requires a GPU but the instance type does not have one. The scientist used the built-in XGBoost algorithm. What should the scientist do to resolve the issue?
Choose a CPU instance type for the training job
XGBoost can run on CPU; use CPU instance.
Install a GPU-enabled version of XGBoost in the training container
Why wrong: XGBoost does not require GPU.
Change the algorithm to a deep learning algorithm
Why wrong: XGBoost is fine for the task.
Use a larger GPU instance type
Why wrong: Using a GPU instance is unnecessary.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Choose a CPU instance type for the training job
XGBoost does not require a GPU; it can run on CPU. The error may be due to using a GPU-only algorithm version or misconfiguration. The simplest solution is to choose a CPU instance type. Installing a GPU version is unnecessary. Changing algorithm is not needed. Using a larger CPU instance can help but is not required. Option A: Choose a CPU instance type is correct. Option B: Installing GPU version is not needed. Option C: Changing algorithm is unnecessary. Option D: Using a larger instance may not address the issue if the instance type is still GPU-only.
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.
Choose a CPU instance type for the training job
Why this is correct
XGBoost can run on CPU; use CPU instance.
Related concept
Read the scenario before looking for a memorised answer.
Install a GPU-enabled version of XGBoost in the training container
Why it's wrong here
XGBoost does not require GPU.
Change the algorithm to a deep learning algorithm
Why it's wrong here
XGBoost is fine for the task.
Use a larger GPU instance type
Why it's wrong here
Using a GPU instance is unnecessary.
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.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Choose a CPU instance type for the training job — XGBoost does not require a GPU; it can run on CPU. The error may be due to using a GPU-only algorithm version or misconfiguration. The simplest solution is to choose a CPU instance type. Installing a GPU version is unnecessary. Changing algorithm is not needed. Using a larger CPU instance can help but is not required. Option A: Choose a CPU instance type is correct. Option B: Installing GPU version is not needed. Option C: Changing algorithm is unnecessary. Option D: Using a larger instance may not address the issue if the instance type is still GPU-only.
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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