MLA-C01 Data Preparation for Machine Learning Practice Question
A data science team is building a model to predict fraudulent transactions. The dataset has 1 million legitimate transactions and only 1,000 fraudulent ones. They plan to use Amazon SageMaker to train a model. Which data preparation technique should they apply to address the severe class imbalance before training?
⚠ Common exam trap
AWS often tests the misconception that simple random oversampling (Option B) is sufficient, but the trap is that it causes overfitting, whereas SMOTE's synthetic generation provides better generalization for imbalanced datasets.
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 SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraudulent samples.
SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic fraudulent samples by interpolating between existing minority class instances in feature space, rather than simply duplicating records. This creates more diverse and realistic training data, reducing overfitting risk while addressing the severe 1:1000 class imbalance. Amazon SageMaker's built-in algorithms and data processing capabilities can easily integrate SMOTE-applied datasets for training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply data augmentation using image transformations because fraud detection is like image classification.
Why it's wrong here
This is a tabular dataset; image augmentation is not applicable.
- ✗
Randomly oversample the fraudulent class to match the legitimate count by duplicating existing fraud records.
Why it's wrong here
Simple duplication risks overfitting and does not add new information.
- ✓
Use SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraudulent samples.
Why this is correct
SMOTE creates synthetic examples by interpolating between existing minority instances, reducing overfitting risk.
- ✗
Randomly undersample the legitimate class to 1,000 samples to create a balanced dataset.
Why it's wrong here
Undersampling discards 999,000 legitimate samples, losing significant information.
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Written by Johnson Ajibi, MSc IT Security
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