MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using SageMaker to build a model for fraud detection. The dataset is highly imbalanced. Which THREE techniques should be applied to address class imbalance?
⚠ Common exam trap
AWS exams often test the misconception that accuracy is a valid metric for imbalanced data, when in fact precision, recall, F1-score, or AUC-ROC are more appropriate.
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 samples of the minority class.
SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which helps balance the dataset without simply duplicating data. This is effective for fraud detection because it provides the model with more diverse examples of fraudulent transactions, reducing the bias toward the majority 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.
- ✗
Train the model only on the majority class.
Why it's wrong here
Ignoring minority class completely would fail to detect fraud.
- ✗
Use accuracy as the evaluation metric.
Why it's wrong here
Accuracy can be high even if minority class is ignored; use precision-recall or AUC.
- ✓
Apply SMOTE to generate synthetic samples of the minority class.
Why this is correct
SMOTE creates synthetic examples to balance classes.
- ✓
Use class weights in the loss function.
Why this is correct
Class weights increase penalty for minority class errors.
- ✓
Undersample the majority class.
Why this is correct
Reducing majority class samples can help balance the dataset.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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