This AI0-001 practice question tests your understanding of ai models and data engineering. 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 training a classification model. The dataset contains 10,000 records with a binary target variable where 9,500 belong to class A and 500 belong to class B. Which technique should the scientist use to address the class imbalance?
SMOTE (Synthetic Minority Oversampling Technique)
SMOTE creates synthetic minority samples to balance classes.
Random undersampling of class A
Why wrong: Undersampling reduces data and may lose important patterns.
Adding Gaussian noise to class B
Why wrong: Adding noise does not create new informative samples.
Principal Component Analysis (PCA)
Why wrong: PCA reduces features, not address imbalance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
SMOTE (Synthetic Minority Oversampling Technique)
SMOTE is the correct technique because it generates synthetic samples for the minority class (class B) by interpolating between existing minority instances, effectively balancing the dataset without losing information. This approach avoids the overfitting risk of simple oversampling and the information loss of undersampling, making it ideal for a 19:1 imbalance ratio.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
SMOTE (Synthetic Minority Oversampling Technique)
Why this is correct
SMOTE creates synthetic minority samples to balance classes.
Related concept
Read the scenario before looking for a memorised answer.
Random undersampling of class A
Why it's wrong here
Undersampling reduces data and may lose important patterns.
Adding Gaussian noise to class B
Why it's wrong here
Adding noise does not create new informative samples.
Principal Component Analysis (PCA)
Why it's wrong here
PCA reduces features, not address imbalance.
Common exam traps
CompTIA often tests the misconception that any data augmentation (like adding noise) or dimensionality reduction (like PCA) can solve class imbalance, when in fact only resampling techniques like SMOTE directly address the skewed distribution of the target variable.
Detailed technical explanation
SMOTE works by selecting a minority class sample, finding its k-nearest neighbors (typically k=5), and generating a new sample along the line segment between the sample and a randomly chosen neighbor. This interpolation creates plausible synthetic instances that expand the decision boundary of the minority class, which is particularly effective for high-dimensional datasets where simple duplication would cause overfitting.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: SMOTE (Synthetic Minority Oversampling Technique) — SMOTE is the correct technique because it generates synthetic samples for the minority class (class B) by interpolating between existing minority instances, effectively balancing the dataset without losing information. This approach avoids the overfitting risk of simple oversampling and the information loss of undersampling, making it ideal for a 19:1 imbalance ratio.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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