- A
Use one-hot encoding on categorical features
Why wrong: One-hot encoding is for categorical variables, not imbalance.
- B
Remove outliers from the transaction amounts
Why wrong: Removing outliers may discard fraud cases; it does not fix class imbalance.
- C
Apply synthetic minority oversampling (SMOTE) to the training set
SMOTE creates synthetic fraud examples, balancing the classes and improving recall on the minority class.
- D
Normalize all numerical features to have zero mean and unit variance
Why wrong: Normalization helps gradient descent but does not address class imbalance.
Question 255 of 1,000
AI0-001 Implementing AI Solutions Practice Question
This AI0-001 practice question tests your understanding of implementing ai solutions. 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 science team is building a binary classifier to detect fraudulent transactions. The dataset has only 2% fraud cases. Which data preparation technique is MOST critical to address this imbalance?
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 synthetic minority oversampling (SMOTE) to the training set
With only 2% fraud cases, the dataset is severely imbalanced, which can cause the classifier to be biased toward the majority class (non-fraud) and achieve high accuracy without learning to detect fraud. SMOTE (Synthetic Minority Oversampling Technique) addresses this by generating synthetic examples of the minority class (fraud) in the training set, balancing the class distribution and improving the model's ability to generalize to fraud cases. This is the most critical technique among the options because it directly tackles the class imbalance problem, which is the primary challenge in this scenario.
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 one-hot encoding on categorical features
Why it's wrong here
One-hot encoding is for categorical variables, not imbalance.
- ✗
Remove outliers from the transaction amounts
Why it's wrong here
Removing outliers may discard fraud cases; it does not fix class imbalance.
- ✓
Apply synthetic minority oversampling (SMOTE) to the training set
Why this is correct
SMOTE creates synthetic fraud examples, balancing the classes and improving recall on the minority class.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Normalize all numerical features to have zero mean and unit variance
Why it's wrong here
Normalization helps gradient descent but does not address class imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that data scaling or encoding is the primary fix for imbalance, when in fact techniques like SMOTE that directly modify the class distribution are required.
Detailed technical explanation
How to think about this question
SMOTE works by selecting a minority class sample, finding its k-nearest neighbors (typically k=5), and creating synthetic samples along the line segments connecting the sample to its neighbors in feature space, rather than simply duplicating existing samples. This avoids overfitting that can occur with random oversampling and introduces more diversity. In real-world fraud detection, SMOTE is often combined with undersampling of the majority class (e.g., using Tomek links) to further clean overlapping regions and improve classifier performance.
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.
Visual reference
What to study next
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Implementing AI Solutions — This question tests Implementing AI Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply synthetic minority oversampling (SMOTE) to the training set — With only 2% fraud cases, the dataset is severely imbalanced, which can cause the classifier to be biased toward the majority class (non-fraud) and achieve high accuracy without learning to detect fraud. SMOTE (Synthetic Minority Oversampling Technique) addresses this by generating synthetic examples of the minority class (fraud) in the training set, balancing the class distribution and improving the model's ability to generalize to fraud cases. This is the most critical technique among the options because it directly tackles the class imbalance problem, which is the primary challenge in this scenario.
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
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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