MLS-C01 Modeling Practice Question
A company wants to build a model to detect fraudulent transactions. The dataset has a highly imbalanced class distribution. Which technique should be used during training to handle class imbalance?
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
✓
Use SageMaker's built-in fraud detection algorithm that applies random under-sampling
Using a fraud detection algorithm that applies random under-sampling is a standard resampling technique to handle class imbalance. AWS SageMaker provides a built-in fraud detection algorithm that incorporates random under-sampling to balance the dataset. Option A is incorrect because adding features does not directly address imbalance. Option C is incorrect as learning rate affects convergence, not imbalance. Option D is incorrect because increasing tree depth alone can lead to overfitting and does not specifically handle class imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add more features to the dataset
Why it's wrong here
Adding more features does not address class imbalance.
- ✓
Use SageMaker's built-in fraud detection algorithm that applies random under-sampling
Why this is correct
Correct. SageMaker's built-in fraud detection algorithm uses random under-sampling, an effective technique for handling class imbalance.
- ✗
Reduce the learning rate
Why it's wrong here
Reducing the learning rate does not mitigate class imbalance.
- ✗
Increase the tree depth in XGBoost
Why it's wrong here
Increasing tree depth is not a recommended technique for class imbalance and may cause overfitting.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.