- A
Reduce the learning rate
Correct. Reducing the learning rate shrinks each tree's contribution, preventing the model from fitting noise too aggressively.
- B
Apply early stopping
Why wrong: Early stopping can reduce overfitting by halting training, but it is not one of the three most direct complexity-control techniques in this selection.
- C
Increase the maximum depth of trees
Why wrong: Increasing tree depth makes the model more complex and worsens overfitting, so it is incorrect.
- D
Increase the regularization parameters (e.g., lambda, alpha)
Correct. Increasing regularization parameters (e.g., lambda, alpha) adds penalties for complexity, directly reducing overfitting.
- E
Add subsampling of data or features
Correct. Subsampling data or features introduces randomness, which reduces overfitting by decorrelating trees.
- F
Increase the number of trees
Why wrong: More trees increase model complexity and risk overfitting.
MLA-C01 Practice Question: A data scientist is developing a gradient…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.)
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
Reduce the learning rate
To reduce overfitting in gradient boosting, apply techniques that control model complexity. Reducing the learning rate (A) makes each tree contribute less, slowing learning. Increasing regularization parameters like lambda and alpha (D) penalizes complexity. Subsampling (E) introduces randomness, reducing correlation between trees. Together, these three directly mitigate overfitting.
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
Correct. Reducing the learning rate shrinks each tree's contribution, preventing the model from fitting noise too aggressively.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply early stopping
Why it's wrong here
Early stopping can reduce overfitting by halting training, but it is not one of the three most direct complexity-control techniques in this selection.
- ✗
Increase the maximum depth of trees
Why it's wrong here
Increasing tree depth makes the model more complex and worsens overfitting, so it is incorrect.
- ✓
Increase the regularization parameters (e.g., lambda, alpha)
Why this is correct
Correct. Increasing regularization parameters (e.g., lambda, alpha) adds penalties for complexity, directly reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Add subsampling of data or features
Why this is correct
Correct. Subsampling data or features introduces randomness, which reduces overfitting by decorrelating trees.
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
The MLA-C01 exam often tests the misconception that increasing model complexity (e.g., deeper trees, more trees) always improves performance, when in fact these changes increase overfitting unless accompanied by countermeasures like regularization or reduced learning rate.
Detailed technical explanation
How to think about this question
Gradient boosting builds trees sequentially, each correcting the errors of the previous ensemble. Early stopping monitors a validation metric (e.g., log loss) and halts training when performance stops improving, directly preventing the model from learning noise in later iterations. Subsampling (stochastic gradient boosting) introduces randomness by using a fraction of data or features per tree, which decorrelates trees and reduces variance, similar to how bagging works in random forests.
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.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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 — To reduce overfitting in gradient boosting, apply techniques that control model complexity. Reducing the learning rate (A) makes each tree contribute less, slowing learning. Increasing regularization parameters like lambda and alpha (D) penalizes complexity. Subsampling (E) introduces randomness, reducing correlation between trees. Together, these three directly mitigate overfitting.
What should I do if I get this MLA-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 →
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Last reviewed: Jul 4, 2026
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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