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 uses Amazon SageMaker to train an XGBoost model on a large dataset. Training takes a long time. Which action can reduce training time without significantly affecting model accuracy?
Use a deep neural network instead
Why wrong: DNNs often take longer.
Increase the learning rate
Why wrong: Higher learning rate may cause instability.
Use a larger instance type
Why wrong: Larger instances speed up but cost more.
Enable early stopping
Early stopping stops when no improvement.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Enable early stopping
Early stopping halts training when the model's performance on a validation set stops improving for a specified number of rounds. This prevents overfitting and reduces training time by eliminating unnecessary iterations, while typically preserving accuracy because the optimal model is already found.
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 deep neural network instead
Why it's wrong here
DNNs often take longer.
Increase the learning rate
Why it's wrong here
Higher learning rate may cause instability.
Use a larger instance type
Why it's wrong here
Larger instances speed up but cost more.
Enable early stopping
Why this is correct
Early stopping stops when no improvement.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
AWS often tests the misconception that increasing learning rate or using more powerful hardware always speeds up training without side effects, but the correct answer focuses on algorithmic efficiency rather than resource scaling.
Detailed technical explanation
XGBoost's early stopping monitors a metric (e.g., log loss or AUC) on a held-out validation set after each boosting round. The training stops if the metric does not improve for a user-defined number of rounds (e.g., early_stopping_rounds=10), which avoids wasted computation on rounds that do not enhance generalization. In practice, this is especially effective for large datasets where overfitting occurs late in training, saving hours without sacrificing model quality.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Enable early stopping — Early stopping halts training when the model's performance on a validation set stops improving for a specified number of rounds. This prevents overfitting and reduces training time by eliminating unnecessary iterations, while typically preserving accuracy because the optimal model is already found.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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