AI0-001 Implementing AI Solutions Practice Question
During data preparation for a classification model, the data scientist notices that one class has 95% of the samples and the other has only 5%. Which technique is MOST appropriate to address this imbalance?
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 SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for the minority class
Resampling techniques like SMOTE generate synthetic samples for the minority class or undersample the majority class, directly addressing 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.
- ✗
Shuffle the data randomly before each training epoch
Why it's wrong here
Shuffling does not change the class distribution; the imbalance remains.
- ✗
Remove the minority class samples entirely
Why it's wrong here
Removing the minority class would eliminate the ability to predict that class, making the model useless for the problem.
- ✗
Use a larger learning rate to force the model to pay attention to the minority class
Why it's wrong here
Learning rate adjustments do not directly address class imbalance; they affect optimization speed, not class distribution.
- ✓
Apply SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for the minority class
Why this is correct
SMOTE creates synthetic minority samples, balancing the dataset and improving model performance without discarding data.
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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.