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?
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
Oversampling the minority class (e.g., SMOTE) or undersampling the majority class are standard techniques to handle imbalanced datasets and improve recall on the minority class.
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 samples for the minority class, balancing the dataset and improving recall.
- ✗
Remove all samples from the majority class to balance the dataset
Why it's wrong here
Removing all majority samples would discard most of the data and cause severe underfitting.
- ✗
Normalize all numerical features to have zero mean and unit variance
Why it's wrong here
Normalization addresses scale differences but does not correct class imbalance.
- ✗
Use a train-test split of 80-20 without any modification
Why it's wrong here
Without addressing imbalance, the model will likely predict the majority class for all instances.
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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.