MLS-C01 Modeling Practice Question
A data scientist is building a binary classification model to predict loan default. The dataset is highly imbalanced (5% default, 95% non-default). Which TWO techniques should the data scientist use to address the class imbalance?
⚠ Common exam trap
AWS often tests the misconception that accuracy is a valid metric for imbalanced datasets, or that undersampling is always preferable to oversampling, when in fact accuracy can be highly misleading and undersampling can discard critical data.
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
✓
Oversample the minority class using SMOTE
Oversampling the minority class using SMOTE (Synthetic Minority Oversampling Technique) is correct because it generates synthetic samples for the minority class by interpolating between existing minority instances, rather than simply duplicating them. This helps balance the dataset without introducing exact copies, which can reduce overfitting and improve the model's ability to generalize to the minority class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Undersample the majority class
Why it's wrong here
Undersampling reduces data, which may lose information; it's a valid technique but not the best here.
- ✗
Use RMSE as the evaluation metric
Why it's wrong here
RMSE is for regression, not classification.
- ✓
Oversample the minority class using SMOTE
Why this is correct
SMOTE generates synthetic samples for the minority class, balancing the dataset.
- ✗
Use accuracy as the evaluation metric
Why it's wrong here
Accuracy is misleading for imbalanced data.
- ✓
Use class weights in the loss function
Why this is correct
Class weights penalize misclassifications of the minority class more heavily.
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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.