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?
⚠ Common exam trap
AI0-001 often tests the misconception that any resampling technique is equally valid for class imbalance, but the key is recognizing that SMOTE is preferred for severe imbalance because it synthesizes new minority samples without discarding majority data, unlike random undersampling which loses information.
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 Over-sampling Technique) is the best choice because it generates new, synthetic minority class samples by interpolating between existing minority instances and their nearest neighbors, rather than simply duplicating them. This directly addresses severe class imbalance (95:5) by enriching the minority class without discarding valuable majority data. Unlike random oversampling, SMOTE reduces the risk of overfitting to exact copies and helps the model learn a more generalizable decision boundary.
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
Min-max normalization rescales feature values to a 0-1 range and does not alter the ratio of majority to minority samples, so the 95:5 imbalance persists. It is tempting because it is a standard preprocessing step. The stem asks specifically for a class-imbalance remedy, such as resampling or class weighting.
- ✗
Random undersampling of the majority class
Why it's wrong here
Random undersampling discards most majority samples, and at 95:5 this throws away roughly 90% of the data, losing information and biasing the model. It is tempting because it is fast and balances classes. The stem's severe imbalance favours oversampling or class weighting instead.
- ✗
Removing all minority class samples
Why it's wrong here
Deleting minority samples eliminates the positive class entirely, leaving nothing for the model to learn; the classifier cannot predict the minority outcome at all. It is tempting as a crude way to remove imbalance. The stem needs the minority class retained and represented, so resampling or weighting applies.
- ✓
SMOTE oversampling of the minority class
Why this is correct
SMOTE generates synthetic minority-class samples by interpolating between existing minority neighbours, rebalancing the 95:5 split without discarding majority data. This gives the classifier more minority examples to learn from, unlike random undersampling which loses information.
About these practice questions
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.