- A
Reduce the amount of training data
Why wrong: Less data typically increases overfitting risk.
- B
Use early stopping based on validation error
Early stopping halts training when validation error stops improving, preventing overfitting.
- C
Increase the maximum depth of the trees in XGBoost
Why wrong: Deeper trees are more complex and prone to overfitting.
- D
Increase the number of training epochs
Why wrong: More epochs can lead to overfitting if not regularized.
- E
Add L1 regularization to the loss function
L1 regularization adds a penalty for large weights, reducing overfitting.
Question 252 of 1,755
MLS-C01 Overfitting 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. A key principle to apply: overfitting. 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.
Which TWO actions can help reduce overfitting when training a model on SageMaker? (Choose TWO.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use early stopping based on validation error
Early stopping (option B) monitors the validation error during training and halts the process when the error stops improving, preventing the model from learning noise in the training data. L1 regularization (option E) adds a penalty proportional to the absolute value of the weights, which encourages sparsity and reduces model complexity, thereby mitigating overfitting. Both techniques directly combat overfitting by constraining the model's capacity.
Key principle: Overfitting
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the amount of training data
Why it's wrong here
Less data typically increases overfitting risk.
- ✓
Use early stopping based on validation error
Why this is correct
Early stopping halts training when validation error stops improving, preventing overfitting.
Related concept
Overfitting
- ✗
Increase the maximum depth of the trees in XGBoost
Why it's wrong here
Deeper trees are more complex and prone to overfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to overfitting if not regularized.
- ✓
Add L1 regularization to the loss function
Why this is correct
L1 regularization adds a penalty for large weights, reducing overfitting.
Related concept
Overfitting
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that adding more data or increasing model complexity (like tree depth or epochs) always improves performance, while underemphasizing the effectiveness of regularization and early stopping. Overfitting is best combated by regularization, validation-based stopping, and appropriate model selection.
Detailed technical explanation
How to think about this question
L1 regularization (Lasso) adds a penalty proportional to the absolute value of the weights, driving some weights to exactly zero and effectively performing feature selection. This sparsity reduces model complexity and helps prevent overfitting by eliminating irrelevant features. In SageMaker's built-in XGBoost, the `alpha` parameter controls L1 regularization, and tuning it is a common practice to combat overfitting in high-dimensional datasets.
KKey Concepts to Remember
- Overfitting
- Early stopping
- L1 regularization
- Validation error
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
Overfitting
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. Overfitting 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.
Review overfitting, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Overfitting.
What is the correct answer to this question?
The correct answer is: Use early stopping based on validation error — Early stopping (option B) monitors the validation error during training and halts the process when the error stops improving, preventing the model from learning noise in the training data. L1 regularization (option E) adds a penalty proportional to the absolute value of the weights, which encourages sparsity and reduces model complexity, thereby mitigating overfitting. Both techniques directly combat overfitting by constraining the model's capacity.
What should I do if I get this MLS-C01 question wrong?
Review overfitting, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Overfitting
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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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