The correct answer is to apply random oversampling on the 'technical' category. This technique directly addresses class imbalance by duplicating examples from the minority class, ensuring the model does not become biased toward the majority 'billing' category during training. In classification tasks, an imbalanced dataset like this one—where 8,000 tickets are billing and only 2,000 are technical—causes the model to predict the majority class too often, sacrificing performance on the underrepresented class. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of preprocessing techniques to handle class imbalance before model training; a common trap is choosing undersampling, which would discard valuable majority data and reduce overall dataset size. Remember the memory tip: "Oversample the underdog"—when the minority class is small, duplicate it to give it equal weight, not delete from the majority.
AI0-001 AI Concepts and Foundations Practice Question
This AI0-001 practice question tests your understanding of ai concepts and foundations. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is training a model to classify customer support tickets into categories. The dataset has 10,000 labeled examples, but the 'billing' category contains 8,000 examples while the 'technical' category contains 2,000. Which technique is most appropriate to address this imbalance before training?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply random oversampling on the 'technical' category.
Option A is correct because random oversampling duplicates examples from the minority class ('technical') to balance the class distribution, preventing the model from becoming biased toward the majority class ('billing'). This technique directly addresses the class imbalance before training, which is critical for classification tasks where the minority class is underrepresented.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 random oversampling on the 'technical' category.
Why this is correct
Correct; oversampling balances the classes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove all examples except 'billing' and use a one-class classifier.
Why it's wrong here
This ignores the 'technical' class entirely.
✗
Use accuracy as the only evaluation metric.
Why it's wrong here
Accuracy is not reliable for imbalanced data.
✗
Train the model as is, then adjust thresholds post-training.
Why it's wrong here
While threshold adjustment can help, addressing imbalance during preprocessing is more direct.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that adjusting thresholds post-training can compensate for class imbalance, but the trap here is that the model's internal weights are already skewed by the imbalanced training data, making threshold tuning ineffective without prior balancing.
Detailed technical explanation
How to think about this question
Random oversampling works by randomly duplicating examples from the minority class until the class counts are equal, which increases the model's exposure to minority patterns but can lead to overfitting if not combined with techniques like SMOTE (Synthetic Minority Oversampling Technique) that generate synthetic samples. In real-world ticket systems, imbalanced data is common because billing issues often outnumber technical ones, and oversampling helps the model learn to identify rare but critical technical issues without losing the majority class information.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply random oversampling on the 'technical' category. — Option A is correct because random oversampling duplicates examples from the minority class ('technical') to balance the class distribution, preventing the model from becoming biased toward the majority class ('billing'). This technique directly addresses the class imbalance before training, which is critical for classification tasks where the minority class is underrepresented.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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