The answer is to add L1 regularization to the loss function and implement early stopping. L1 regularization, also known as Lasso, works by adding a penalty proportional to the absolute value of the model weights, effectively shrinking less important feature coefficients to zero and simplifying the model to prevent it from fitting noise. Early stopping complements this by monitoring validation error during training; when the error stops improving, training halts immediately, ensuring the model does not continue learning spurious patterns after generalization has peaked. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of overfitting reduction techniques within SageMaker’s built-in training capabilities. A common trap is confusing L1 with L2 regularization—L2 shrinks weights but rarely zeros them out, making L1 more aggressive for feature selection. Another pitfall is thinking early stopping only saves time; its primary purpose is to prevent overfitting by cutting off training at the optimal generalization point. Memory tip: L1 “Lasso” leaves less—it zeros out features, while early stopping stops the “over” in overfitting.
MLS-C01 Modeling 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. 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.
Which TWO actions can help reduce overfitting when training a model on SageMaker? (Choose TWO.)
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 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. This directly reduces overfitting by ensuring the model does not continue to fit to spurious patterns after generalization has peaked.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Read the scenario before looking for a memorised answer.
✗
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
Read the scenario before looking for a memorised answer.
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, when in fact these actions typically worsen overfitting without proper validation or regularization.
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
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use early stopping based on validation error — Early stopping 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. This directly reduces overfitting by ensuring the model does not continue to fit to spurious patterns after generalization has peaked.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO actions can help reduce overfitting in a neural network? (Choose 2.)
medium
A.Increase the number of layers.
B.Decrease the learning rate.
✓ C.Apply L1 or L2 regularization.
D.Increase the training dataset size.
✓ E.Add dropout layers.
Why C: Option A is correct because dropout randomly drops units, preventing co-adaptation. Option D is correct because L1/L2 regularization penalize large weights. Option B is wrong because adding more layers increases model complexity. Option C is wrong because increasing training data helps underfitting, not overfitting. Option E is wrong because reducing learning rate may not prevent overfitting.
Variation 2. Which TWO actions can help reduce overfitting in a decision tree model? (Choose 2.)
medium
✓ A.Prune the tree after training
B.Increase the maximum depth of the tree
✓ C.Set a minimum number of samples per leaf
D.Increase the number of features considered at each split
E.Use all training data without validation
Why A: Pruning the tree after training removes branches that have little predictive power, reducing overfitting by simplifying the model. This technique directly addresses the variance component of the bias-variance tradeoff, making the model generalize better to unseen data.
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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.
Question Discussion
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