AI0-001 Implementing AI Solutions Practice Question
A data science team is preparing a dataset for a binary classification model. The dataset has 95% negative class and 5% positive class. Which technique should they apply to avoid biased model predictions?
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 resampling techniques such as SMOTE or random undersampling
Handling imbalanced data (e.g., oversampling the minority class or undersampling the majority class) is necessary to prevent the model from always predicting the majority 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 resampling techniques such as SMOTE or random undersampling
Why this is correct
Resampling balances the class distribution, allowing the model to learn from both classes effectively.
- ✗
Normalise all numerical features to a [0,1] range
Why it's wrong here
Normalisation helps with convergence but does not address class imbalance.
- ✗
Shuffle the dataset randomly before splitting into train and test sets
Why it's wrong here
Shuffling ensures randomness but does not address imbalance.
- ✗
Remove all rows with missing values
Why it's wrong here
Handling missing values is important but does not solve class imbalance.
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JA
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.