- A
Increase the value of the max_depth hyperparameter.
Why wrong: Increasing max_depth makes trees deeper, increasing model complexity and overfitting.
- B
Increase the value of the subsample hyperparameter to 1.0.
Why wrong: Subsample greater than 1.0 is invalid; subsample=1 uses all data, which may not reduce overfitting.
- C
Increase the value of the lambda (L2 regularization) hyperparameter.
L2 regularization penalizes large coefficients, reducing model complexity and overfitting.
- D
Increase the value of the num_round hyperparameter.
Why wrong: More boosting rounds can lead to overfitting by fitting noise.
Question 549 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 trains a model using Amazon SageMaker's built-in XGBoost algorithm. The model overfits on the training data. Which hyperparameter adjustment is MOST likely to reduce overfitting?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 value of the lambda (L2 regularization) hyperparameter.
Increasing the lambda (L2 regularization) hyperparameter adds a penalty on the squared magnitude of the model weights, which discourages the model from fitting noise in the training data. This directly reduces overfitting by shrinking the influence of individual features, a standard regularization technique in XGBoost.
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.
- ✗
Increase the value of the max_depth hyperparameter.
Why it's wrong here
Increasing max_depth makes trees deeper, increasing model complexity and overfitting.
- ✗
Increase the value of the subsample hyperparameter to 1.0.
Why it's wrong here
Subsample greater than 1.0 is invalid; subsample=1 uses all data, which may not reduce overfitting.
- ✓
Increase the value of the lambda (L2 regularization) hyperparameter.
Why this is correct
L2 regularization penalizes large coefficients, reducing model complexity and overfitting.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the value of the num_round hyperparameter.
Why it's wrong here
More boosting rounds can lead to overfitting by fitting noise.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS exam candidates often mistakenly think that increasing any hyperparameter that adds complexity (like max_depth or num_round) can reduce overfitting, when in fact only regularization parameters or those that reduce model capacity are effective.
Detailed technical explanation
How to think about this question
L2 regularization (lambda) in XGBoost is applied to the leaf weights during tree construction, penalizing large weights and effectively smoothing the model's output. This is analogous to ridge regression in linear models and is particularly effective when features are highly correlated or when the dataset has high dimensionality. In practice, tuning lambda alongside alpha (L1 regularization) and gamma (minimum loss reduction for a split) provides a powerful set of controls against overfitting.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the value of the lambda (L2 regularization) hyperparameter. — Increasing the lambda (L2 regularization) hyperparameter adds a penalty on the squared magnitude of the model weights, which discourages the model from fitting noise in the training data. This directly reduces overfitting by shrinking the influence of individual features, a standard regularization technique in XGBoost.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 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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