AI0-001 Implementing AI Solutions Practice Question
A data scientist is preparing a dataset for a binary classification model. The dataset has 95% majority class and 5% minority class. Which data preparation technique is BEST to address the class imbalance?
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
✓
SMOTE oversampling of the minority class
SMOTE (Synthetic Minority Oversampling TEchnique) generates synthetic samples for the minority class, balancing the dataset without simply duplicating existing minority instances.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Min-max normalization of all features
Why it's wrong here
Normalization scales features but does not address class imbalance; the model would still be biased toward the majority class.
- ✗
Random undersampling of the majority class
Why it's wrong here
Undersampling discards many majority class samples, which can lead to loss of important information and reduced model performance.
- ✗
Removing all minority class samples
Why it's wrong here
Removing the minority class eliminates the problem but also removes any ability to detect the minority class, which is usually the target of interest.
- ✓
SMOTE oversampling of the minority class
Why this is correct
SMOTE creates synthetic minority samples by interpolating between existing minority instances, effectively balancing the classes without losing data.
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Written by Johnson Ajibi, MSc IT Security
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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.