AI0-001 Implementing AI Solutions Practice Question
During the data preparation phase of an AI project, a data scientist discovers that the target variable in a binary classification dataset is heavily imbalanced: 95% negative class and 5% positive class. Which technique should be applied to improve model performance on the minority class?
⚠ Common exam trap
AI0-001 often tests the misconception that accuracy is a good metric for imbalanced data, or that simple removal of majority class is acceptable; candidates might overlook the need for specialized resampling techniques.
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 oversampling of the minority class using techniques like SMOTE
The dataset is heavily imbalanced (95% negative, 5% positive), which can cause models to be biased toward the majority class. Oversampling the minority class using techniques like SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic examples of the minority class, balancing the class distribution and improving the model's ability to learn the minority class patterns. This is a standard approach to address 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.
- ✓
Apply oversampling of the minority class using techniques like SMOTE
Why this is correct
SMOTE generates synthetic minority-class samples by interpolating between existing positive instances and their nearest neighbours, rebalancing the 95:5 distribution so the classifier no longer biases towards the majority class. This directly targets the minority-class performance constraint stated in the stem.
- ✗
Remove all samples from the majority class to balance the dataset
Why it's wrong here
Deleting majority samples discards most of the available information and risks severe overfitting to the few remaining negatives. Undersampling is chosen when the majority class is enormous and redundant, not when every retained record carries useful signal.
- ✗
Normalize all numerical features to have zero mean and unit variance
Why it's wrong here
Feature scaling changes only the magnitude of numerical inputs; it leaves the 95:5 class ratio untouched, so minority-class performance does not improve. Normalisation is the right step for distance-based or gradient-based algorithms when features span wildly different ranges.
- ✗
Use a train-test split of 80-20 without any modification
Why it's wrong here
An untouched 80-20 split preserves the 95:5 imbalance, so the classifier still favours the majority class and performs poorly on positives. Such a split is standard once resampling, class weights or threshold tuning have already addressed the imbalance.
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JA
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.