Question 1,005 of 1,672
MLS-C01 Modeling Practice Question
A machine learning engineer is tuning hyperparameters for a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is validation accuracy. After several tuning jobs, the best accuracy achieved is 0.85, but the engineer suspects the model is overfitting. Which hyperparameter adjustment is most likely to reduce overfitting?
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
✓
Increase the regularization parameter (e.g., lambda or alpha)
Increasing the regularization parameter (e.g., lambda or alpha in XGBoost) penalizes model complexity and helps reduce overfitting, making option A correct. Option B is incorrect because increasing maximum depth increases model complexity, leading to overfitting. Option C is incorrect because increasing subsample ratio (using more data per tree) can increase overfitting, while decreasing it often reduces overfitting. Option D is incorrect because increasing learning rate makes the model learn faster, which can lead to 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.
- ✓
Increase the regularization parameter (e.g., lambda or alpha)
Why this is correct
Regularization penalizes large weights, reducing overfitting.
- ✗
Increase the maximum depth of trees
Why it's wrong here
Deeper trees increase model complexity, often leading to overfitting.
- ✗
Increase the subsample ratio
Why it's wrong here
Increasing subsample ratio means using more data per iteration, which can increase overfitting.
- ✗
Increase the learning rate
Why it's wrong here
A higher learning rate can cause the model to overfit by fitting noise more aggressively.
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 |
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 →
Last reviewed: Jun 20, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.