Quick Answer
The correct choice is to apply Principal Component Analysis (PCA) to reduce the number of features. This directly addresses the core bottleneck: with 500 features and a million rows, XGBoost’s tree-building process must evaluate splits across a high-dimensional space, which dramatically increases computation time. PCA reduces dimensionality by projecting the data onto a smaller set of uncorrelated components that capture most of the variance, thereby speeding up training while preserving the predictive signal needed for accuracy. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of feature engineering trade-offs—specifically, that PCA is a linear, unsupervised technique that can drastically cut training time without sacrificing model performance, unlike reducing tree count or using a smaller instance, which risk underfitting. A common trap is to assume that increasing instances or using cheaper hardware will solve the problem, but these either increase cost or degrade accuracy. Memory tip: “PCA trims the fat, XGBoost runs like that.”
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 training a model using SageMaker's built-in XGBoost algorithm. The dataset has 500 features and 1 million rows. The training job is taking too long. The scientist wants to reduce training time without sacrificing accuracy. Which action is LIKELY to be most effective?
Use a smaller instance type to reduce time
Why wrong: Smaller instance may increase training time.
Reduce the number of trees in XGBoost
Why wrong: Reducing trees may reduce accuracy.
Use a larger instance type with more vCPUs
Why wrong: Larger instance may speed up but increase cost.
Apply Principal Component Analysis (PCA) to reduce the number of features
PCA reduces dimensionality, speeding up training while retaining most information.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Apply Principal Component Analysis (PCA) to reduce the number of features
Option A (Reduce the number of features by applying PCA) is correct because it reduces dimensionality, speeding up training. Option B (Increase the number of instances) may not be cost-effective. Option C (Use a smaller instance) may reduce time but also accuracy. Option D (Reduce the number of trees) may reduce accuracy.
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.
Use a smaller instance type to reduce time
Why it's wrong here
Smaller instance may increase training time.
Reduce the number of trees in XGBoost
Why it's wrong here
Reducing trees may reduce accuracy.
Use a larger instance type with more vCPUs
Why it's wrong here
Larger instance may speed up but increase cost.
Apply Principal Component Analysis (PCA) to reduce the number of features
Why this is correct
PCA reduces dimensionality, speeding up training while retaining most information.
Related concept
Read the scenario before looking for a memorised answer.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Apply Principal Component Analysis (PCA) to reduce the number of features — Option A (Reduce the number of features by applying PCA) is correct because it reduces dimensionality, speeding up training. Option B (Increase the number of instances) may not be cost-effective. Option C (Use a smaller instance) may reduce time but also accuracy. Option D (Reduce the number of trees) may reduce accuracy.
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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