Quick Answer
The correct action is to set early stopping based on validation loss. This directly addresses the classic overfitting pattern where training loss continues to decrease while validation loss begins to rise, indicating the model is memorizing noise rather than learning generalizable patterns. XGBoost early stopping overfitting prevention works by monitoring the evaluation metric on a held-out validation set; when the metric fails to improve for a specified number of rounds (set via the `early_stopping_rounds` parameter in SageMaker), training halts automatically. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of regularization techniques specific to gradient boosting, often appearing as a distractor-laden question where candidates might mistakenly choose to increase tree depth or reduce the learning rate. A common trap is confusing early stopping with reducing overfitting after training has already occurred—early stopping is a proactive, real-time guardrail. Memory tip: think “stop when val loss stops dropping” to distinguish it from other hyperparameter tuning approaches.
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 data scientist is training a binary classification model using SageMaker XGBoost and notices that training loss decreases but validation loss increases after a few epochs. Which action should the data scientist take to address this issue?
Increase the number of rounds
Why wrong: Would continue overfitting.
Set early stopping based on validation loss
Stops training when validation loss stops improving.
Increase the learning rate
Why wrong: May cause divergence.
Increase the maximum tree depth
Why wrong: Would increase overfitting.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Set early stopping based on validation loss
The increasing validation loss while training loss decreases is a classic sign of overfitting. Setting early stopping based on validation loss halts training when the validation loss stops improving, preventing the model from memorizing noise in the training data. SageMaker XGBoost's `early_stopping_rounds` parameter monitors the evaluation metric on the validation set and stops training if no improvement is seen for a specified number of rounds.
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.
Increase the number of rounds
Why it's wrong here
Would continue overfitting.
Set early stopping based on validation loss
Why this is correct
Stops training when validation loss stops improving.
Related concept
Read the scenario before looking for a memorised answer.
Increase the learning rate
Why it's wrong here
May cause divergence.
Increase the maximum tree depth
Why it's wrong here
Would increase overfitting.
Common exam traps
AWS often tests the misconception that increasing model complexity (more rounds, deeper trees, higher learning rate) always improves performance, when in fact these actions worsen overfitting when validation loss diverges from training loss.
Detailed technical explanation
Under the hood, XGBoost uses a gradient boosting framework where each tree corrects errors of the previous ensemble. Overfitting occurs when the model learns training-specific noise; early stopping acts as a regularization technique by monitoring a held-out validation set. In practice, setting `early_stopping_rounds` to a value like 10 or 20, combined with a separate validation set, is a standard approach to automatically determine the optimal number of boosting rounds without manual tuning.
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Set early stopping based on validation loss — The increasing validation loss while training loss decreases is a classic sign of overfitting. Setting early stopping based on validation loss halts training when the validation loss stops improving, preventing the model from memorizing noise in the training data. SageMaker XGBoost's `early_stopping_rounds` parameter monitors the evaluation metric on the validation set and stops training if no improvement is seen for a specified number of rounds.
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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