- A
Use a linear regression model
Why wrong: Linear regression is for continuous output, not classification.
- B
Oversample the minority class
Oversampling creates synthetic instances of the minority class, helping the model learn better boundaries.
- C
Undersample the majority class
Why wrong: Undersampling discards majority class data, which may lose valuable information and cause bias.
- D
Increase the learning rate
Why wrong: Learning rate affects training speed, not class imbalance.
Question 826 of 1,000
Techniques for Handling Class Imbalance in Machine Learning
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraud cases. Which technique is most appropriate to address the class imbalance?
Quick Answer
The answer is to oversample the minority class, most commonly using a technique like SMOTE (Synthetic Minority Oversampling Technique). This approach is correct because it directly addresses class imbalance by generating synthetic examples of the minority class—in this case, fraudulent transactions—rather than simply duplicating existing records. By creating new, plausible data points in the feature space, oversampling balances the dataset without discarding valuable majority-class information, which is critical when the minority class represents only 1% of the data. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of data preprocessing for imbalanced classification, a core concept in real-world AI applications like fraud detection. A common trap is choosing undersampling, which discards majority-class data and risks losing signal, or using accuracy as a metric, which is misleading with severe imbalance. Remember the mnemonic “SMOTE the minority, don’t drown the majority”—oversampling creates new data, not just copies.
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
✓
Oversample the minority class
Oversampling the minority class (e.g., using SMOTE or random oversampling) is the most appropriate technique because it balances the dataset by generating synthetic or duplicate examples of the fraud cases, allowing the model to learn the decision boundary for the minority class without discarding valuable majority-class data. This directly addresses the class imbalance where only 1% of transactions are fraudulent, improving recall and precision for fraud detection.
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.
- ✗
Use a linear regression model
Why it's wrong here
Linear regression is for continuous output, not classification.
- ✓
Oversample the minority class
Why this is correct
Oversampling creates synthetic instances of the minority class, helping the model learn better boundaries.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Undersample the majority class
Why it's wrong here
Undersampling discards majority class data, which may lose valuable information and cause bias.
- ✗
Increase the learning rate
Why it's wrong here
Learning rate affects training speed, not class imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that undersampling is always better because it reduces dataset size and training time, but the trap here is that undersampling discards majority-class data, which can severely degrade model performance when the imbalance is extreme (e.g., 1:99 ratio).
Trap categories for this question
Command / output trap
Linear regression is for continuous output, not classification.
Detailed technical explanation
How to think about this question
Oversampling techniques like SMOTE (Synthetic Minority Over-sampling Technique) create synthetic samples by interpolating between existing minority-class instances in feature space, which reduces overfitting compared to simple duplication. In fraud detection, this is critical because the minority class (fraud) often has complex, non-linear patterns that require sufficient representative samples for the model to learn effectively. A real-world scenario involves credit card transaction datasets where fraud is rare but costly, and oversampling helps the model avoid always predicting 'non-fraud' (accuracy paradox).
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?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Oversample the minority class — Oversampling the minority class (e.g., using SMOTE or random oversampling) is the most appropriate technique because it balances the dataset by generating synthetic or duplicate examples of the fraud cases, allowing the model to learn the decision boundary for the minority class without discarding valuable majority-class data. This directly addresses the class imbalance where only 1% of transactions are fraudulent, improving recall and precision for fraud detection.
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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Last reviewed: Jul 4, 2026
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