AI0-001 AI Models and Data Engineering Practice Question
A data scientist is preparing a dataset for training a classification model. The dataset contains 10,000 records with a binary target variable where 9,500 belong to class A and 500 belong to class B. Which technique should the scientist use to address the class imbalance?
⚠ Common exam trap
CompTIA often tests the misconception that any data augmentation (like adding noise) or dimensionality reduction (like PCA) can solve class imbalance, when in fact only resampling techniques like SMOTE directly address the skewed distribution of the target variable.
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 (Synthetic Minority Oversampling Technique)
SMOTE is the correct technique because it generates synthetic samples for the minority class (class B) by interpolating between existing minority instances, effectively balancing the dataset without losing information. This approach avoids the overfitting risk of simple oversampling and the information loss of undersampling, making it ideal for a 19:1 imbalance ratio.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SMOTE (Synthetic Minority Oversampling Technique)
Why this is correct
SMOTE creates synthetic minority samples to balance classes.
- ✗
Random undersampling of class A
Why it's wrong here
Undersampling reduces data and may lose important patterns.
- ✗
Adding Gaussian noise to class B
Why it's wrong here
Adding noise does not create new informative samples.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA reduces features, not address imbalance.
About these practice questions
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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.