MLS-C01 Practice Question: Machine Learning Implementation and Operations
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?
⚠ Common exam trap
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.
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.
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.
- ✗
Increase the value of the num_round hyperparameter.
Why it's wrong here
More boosting rounds can lead to overfitting by fitting noise.
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 |
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