AI0-001 Implementing AI Solutions Practice Question
A data scientist is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset has 1% fraud cases (minority class) and 99% non-fraud cases. Which data preparation technique is MOST appropriate to address the class imbalance before training?
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 (Synthetic Minority Over-sampling Technique) to the minority class
Synthetic Minority Over-sampling Technique (SMOTE) generates synthetic samples for the minority class, balancing the dataset without losing data. Undersampling would discard valuable majority samples, and oversampling by duplication can cause overfitting. Normalization does not fix 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.
- ✗
Duplicate the minority class samples until the class ratio is 50:50
Why it's wrong here
Simple duplication can lead to overfitting and does not introduce new variance like SMOTE does.
- ✗
Normalize all features to a range of 0 to 1
Why it's wrong here
Normalization helps numerical stability but does not address class imbalance.
- ✗
Random undersample the majority class to match the minority class size
Why it's wrong here
Undersampling discards many majority samples, losing potentially valuable information and reducing model performance.
- ✓
Apply SMOTE (Synthetic Minority Over-sampling Technique) to the minority class
Why this is correct
SMOTE creates synthetic examples for the minority class, effectively balancing the dataset without simply duplicating existing samples.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.