The answer is to reduce the learning rate, add subsampling of data or features, and implement early stopping. These three techniques directly combat overfitting in gradient boosting by controlling how aggressively the model learns from residual errors. A lower learning rate forces the model to take smaller, more cautious steps, while subsampling introduces randomness by training each tree on a random subset of data or features, reducing variance. Early stopping halts training once validation performance stops improving, preventing the model from memorizing noise. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of bias-variance tradeoff in ensemble methods; a common trap is confusing complexity-increasing actions like adding more trees or depth with regularization. Remember the mnemonic “LSE” for Learning rate, Subsampling, and Early stopping—these are your three levers to tame overfitting without sacrificing predictive power.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 developing a gradient boosting model and observes that the model is overfitting to the training data. Which three techniques can help reduce overfitting? (Select THREE.)
A lower learning rate makes the model more robust and reduces overfitting.
B
Apply early stopping
Early stopping halts training when validation performance stops improving, preventing overfitting.
C
Increase the maximum depth of trees
Why wrong: Deeper trees capture more patterns and increase overfitting.
D
Increase the regularization parameters (e.g., lambda, alpha)
Why wrong: Regularization parameters help reduce overfitting, but we need exactly three; the combination of B, D, E is most directly associated with gradient boosting.
E
Add subsampling of data or features
Subsampling introduces randomness, which acts as a regularizer.
F
Increase the number of trees
Why wrong: More trees increase model complexity and risk overfitting.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce the learning rate
Reducing the learning rate slows down learning and helps reduce overfitting. Subsampling (data/feature sampling) adds randomness and reduces overfitting. Early stopping stops training before overfitting occurs. Increasing the number of trees or tree depth increases model complexity, worsening overfitting. Increasing regularization parameters (like lambda, alpha) also helps reduce overfitting, but the three most common for gradient boosting are reducing learning rate, subsampling, and early stopping.
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 learning rate
Why this is correct
A lower learning rate makes the model more robust and reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✓
Apply early stopping
Why this is correct
Early stopping halts training when validation performance stops improving, preventing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the maximum depth of trees
Why it's wrong here
Deeper trees capture more patterns and increase overfitting.
✗
Increase the regularization parameters (e.g., lambda, alpha)
Why it's wrong here
Regularization parameters help reduce overfitting, but we need exactly three; the combination of B, D, E is most directly associated with gradient boosting.
✓
Add subsampling of data or features
Why this is correct
Subsampling introduces randomness, which acts as a regularizer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of trees
Why it's wrong here
More trees increase model complexity and risk overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLA-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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce the learning rate — Reducing the learning rate slows down learning and helps reduce overfitting. Subsampling (data/feature sampling) adds randomness and reduces overfitting. Early stopping stops training before overfitting occurs. Increasing the number of trees or tree depth increases model complexity, worsening overfitting. Increasing regularization parameters (like lambda, alpha) also helps reduce overfitting, but the three most common for gradient boosting are reducing learning rate, subsampling, and early stopping.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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.
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. A team trained a gradient boosting model with the following hyperparameters: learning_rate=0.1, n_estimators=1000, max_depth=6. The model achieves excellent training accuracy but poor validation accuracy. They suspect overfitting. Which hyperparameter change is LEAST likely to help?
hard
✓ A.Increase learning_rate to 0.5
B.Decrease n_estimators to 100
C.Add a subsample fraction of 0.8
D.Decrease max_depth to 3
Why A: Increasing the learning rate makes the model more aggressive and can worsen overfitting. Decreasing n_estimators, decreasing max_depth, and adding subsampling all reduce model complexity and help mitigate overfitting.
Last reviewed: Jun 23, 2026
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This MLA-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 MLA-C01 exam.
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