- A
Remove the minority class samples to make the dataset balanced
Why wrong: Removing samples discards valuable data and reduces model's ability to learn the minority class.
- B
Use a stratified train-test split to preserve class proportions
Stratified split ensures both training and test sets have similar class ratios.
- C
Apply SMOTE (Synthetic Minority Over-sampling Technique) to balance the training set
SMOTE generates synthetic samples for the minority class, reducing bias towards the majority class.
- D
Report only accuracy as the evaluation metric
Why wrong: Accuracy is misleading for imbalanced data.
- E
Use precision, recall, and F1-score for evaluation
These metrics are more informative for imbalanced datasets.
Question 569 of 1,000
AI0-001 AI Concepts and Techniques Practice Question
This AI0-001 practice question tests your understanding of ai concepts and techniques. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 preparing a dataset for a binary classification model. The dataset has 1000 samples, with 800 positives and 200 negatives. To evaluate the model properly, which THREE steps should they take? (Select THREE)
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
✓
Use a stratified train-test split to preserve class proportions
Option B is correct because stratified train-test splitting ensures that the class distribution (80% positive, 20% negative) is preserved in both training and test sets. This prevents the model from being evaluated on a test set that has a different class ratio, which could give a misleading impression of performance, especially in imbalanced datasets.
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.
- ✗
Remove the minority class samples to make the dataset balanced
Why it's wrong here
Removing samples discards valuable data and reduces model's ability to learn the minority class.
- ✓
Use a stratified train-test split to preserve class proportions
Why this is correct
Stratified split ensures both training and test sets have similar class ratios.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Apply SMOTE (Synthetic Minority Over-sampling Technique) to balance the training set
Why this is correct
SMOTE generates synthetic samples for the minority class, reducing bias towards the majority class.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Report only accuracy as the evaluation metric
Why it's wrong here
Accuracy is misleading for imbalanced data.
- ✓
Use precision, recall, and F1-score for evaluation
Why this is correct
These metrics are more informative for imbalanced datasets.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that removing minority samples or relying solely on accuracy is acceptable for imbalanced datasets, when in fact these approaches degrade model performance and evaluation validity.
Detailed technical explanation
How to think about this question
Stratified splitting works by performing random sampling within each class stratum, ensuring that the proportion of each class is maintained exactly in both splits. SMOTE generates synthetic minority samples by interpolating between existing minority instances and their k-nearest neighbors, which helps the model learn decision boundaries without simply duplicating data. In real-world scenarios like fraud detection, where positives (fraud) are rare, using stratified splits and SMOTE can significantly improve recall without sacrificing precision.
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.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Concepts and Techniques — This question tests AI Concepts and Techniques — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a stratified train-test split to preserve class proportions — Option B is correct because stratified train-test splitting ensures that the class distribution (80% positive, 20% negative) is preserved in both training and test sets. This prevents the model from being evaluated on a test set that has a different class ratio, which could give a misleading impression of performance, especially in imbalanced datasets.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
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.
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