AI0-001 AI Models and Data Engineering Practice Question
A model trained on a dataset with imbalanced classes achieves 98% accuracy but only 50% recall for the minority class. Which technique should be applied first to address the imbalance?
⚠ Common exam trap
CompTIA often tests the misconception that data-level techniques like SMOTE or undersampling should always be the first approach, when in fact cost-sensitive learning is a simpler, less invasive, and often more effective initial step that directly adjusts the model's learning objective.
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 cost-sensitive learning
Cost-sensitive learning directly modifies the model's loss function to penalize misclassifications of the minority class more heavily than those of the majority class. This approach addresses the root cause of the imbalance—the model's bias toward the majority class—without altering the dataset distribution, making it the most immediate and effective first step.
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 cost-sensitive learning
Why this is correct
Cost-sensitive learning adjusts class weights in the loss function, directly tackling imbalance without data modification.
- ✗
Reduce the majority class size
Why it's wrong here
Undersampling may discard valuable data and reduce overall performance.
- ✗
Use SMOTE to generate synthetic samples
Why it's wrong here
SMOTE generates synthetic minority samples to balance class distribution, but the question asks which technique should be applied *first*. Addressing class imbalance should begin with data-level methods like random undersampling or oversampling, as SMOTE introduces synthetic neighbours that risk overfitting or noise when the minority class is extremely sparse. It is tempting because SMOTE directly increases minority representation, and would be correct after initial resampling has established a baseline.
- ✗
Collect more data for the minority class
Why it's wrong here
Although beneficial, collecting additional data is often not the most immediate or feasible option.
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.