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?
⚠ Common exam trap
AI0-001 often tests whether candidates confuse data-level techniques (SMOTE, resampling) with algorithm-level techniques (class weights, focal loss) — a larger learning rate is a tempting but incorrect 'make the model care' answer.
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
SMOTE generates synthetic samples of the minority class by interpolating between existing minority instances in feature space, which balances the class distribution and prevents the model from ignoring the minority class. With a 95/5 split, standard classifiers tend to predict the majority class almost exclusively, achieving high accuracy but poor recall on the minority class. SMOTE is the standard, well-established technique for this exact scenario.
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 reorders samples each epoch but preserves the 95/5 ratio, so the loss remains dominated by the majority class. It is tempting because shuffling is a real and necessary practise to prevent order-dependent bias, and it would be the right choice when training data is sorted by label or time.
- ✗
Remove the minority class samples entirely
Why it's wrong here
Deleting the minority samples removes the 5% class entirely, leaving a single-class dataset the classifier cannot learn to separate. It is tempting because undersampling the majority class is a legitimate imbalance technique, and removing majority samples would be correct when the majority is noisy or redundant.
- ✗
Use a larger learning rate to force the model to pay attention to the minority class
Why it's wrong here
A larger learning rate alters gradient step size, not class weighting, so the 95/5 split still dominates the loss and the minority class stays underfitted. It is tempting because learning rate is a genuine tuning lever, and raising it is correct when training converges too slowly or stalls in a plateau.
- ✓
Apply SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for the minority class
Why this is correct
SMOTE interpolates new minority-class points between existing minority neighbours, directly correcting the 95/5 skew. Unlike random oversampling, it does not merely duplicate rows, reducing overfitting risk. This addresses the stated class imbalance before training the classifier.
About these practice questions
This AI0-001 question is part of Courseiva's 962-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.