MLS-C01 Modeling Practice Question
A data scientist is tuning a gradient boosting model using Amazon SageMaker's Automatic Model Tuning (hyperparameter optimization). The objective metric is validation:auc. After 50 training jobs, the best model still has a validation AUC of only 0.65. The scientist suspects overfitting because the training AUC is 0.99. Which hyperparameter configuration is MOST likely to reduce overfitting?
⚠ Common exam trap
Many candidates assume increasing model complexity (e.g., more rounds, deeper trees) will improve performance, but the question explicitly describes overfitting, so the correct answer must reduce complexity or increase regularization, which is lambda.
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 lambda from 1 to 10
Increasing lambda (L2 regularization) from 1 to 10 adds a stronger penalty on the magnitude of leaf weights in the gradient boosting model. This directly reduces overfitting by discouraging the model from fitting noise in the training data, which is consistent with the observed gap between training AUC (0.99) and validation AUC (0.65). In XGBoost, lambda controls the L2 regularization term on weights, and a higher value forces the model to be simpler and more generalizable.
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 lambda from 1 to 10
Why this is correct
Higher L2 regularization reduces overfitting by penalizing large weights.
- ✗
Increase num_round from 100 to 500
Why it's wrong here
More boosting rounds increase model complexity, worsening overfitting.
- ✗
Increase max_depth from 6 to 12
Why it's wrong here
Deeper trees increase complexity and overfitting.
- ✗
Increase subsample from 0.5 to 1.0
Why it's wrong here
Higher subsample means using more data per tree, which can increase overfitting if already overfitting.
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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