MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million transactions, with 0.1% fraud. The data scientist trains a random forest model and achieves 99.9% accuracy but 0% recall on the fraud class. Which technique is most likely to improve recall without significantly reducing precision?
⚠ Common exam trap
Watch out — candidates often assume cost-sensitive learning (Option B) is the best approach for imbalanced data, but in extreme imbalance with 0% recall, oversampling techniques like SMOTE are more effective because they directly increase the minority class representation rather than just adjusting penalties.
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
✓
Apply SMOTE to generate synthetic fraud samples
SMOTE (Synthetic Minority Oversampling Technique) generates synthetic fraud samples by interpolating between existing minority class instances, which directly addresses the extreme class imbalance (0.1% fraud). This increases the representation of the fraud class in the training data, allowing the random forest model to learn decision boundaries that capture fraud patterns, thereby improving recall without introducing the noise or information loss associated with other methods.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Tune the classification threshold
Why it's wrong here
Threshold tuning can improve recall but often at the cost of precision; it doesn't directly address data imbalance.
- ✗
Use cost-sensitive learning with a high cost for fraud misclassification
Why it's wrong here
Cost-sensitive learning can improve recall but may significantly reduce precision if the cost is too high.
- ✓
Apply SMOTE to generate synthetic fraud samples
Why this is correct
SMOTE creates synthetic instances of the minority class, balancing the dataset and improving recall while maintaining precision.
- ✗
Undersample the majority class
Why it's wrong here
Undersampling reduces the dataset size significantly, potentially losing valuable information and harming precision.
Go deeper
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.