AI0-001 AI Concepts and Foundations Practice Question
An AI model for detecting fraudulent transactions has high precision but low recall. Which business impact is most likely?
⚠ Common exam trap
CompTIA often tests the distinction between precision and recall by presenting a scenario where candidates confuse high precision with high recall, leading them to incorrectly select option D (many legitimate transactions flagged) instead of recognizing that low recall causes undetected fraud.
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
✓
Many fraudulent transactions go undetected
High precision means that when the model flags a transaction as fraudulent, it is very likely correct. However, low recall indicates that the model misses a significant proportion of actual fraudulent transactions. Therefore, the most likely business impact is that many fraudulent transactions go undetected, leading to financial losses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model has no impact on fraud detection
Why it's wrong here
Low recall still catches some fraud correctly, so the model does affect detection; it simply misses a substantial portion of fraudulent cases. It is tempting to dismiss the model entirely, but a high-precision model produces reliable alerts on the fraud it does identify, making it useful despite incomplete coverage.
- ✗
The model detects all fraudulent transactions
Why it's wrong here
High precision means few false positives, not full coverage; low recall means many fraudulent transactions go undetected, so the model misses most fraud rather than catching all of it. It is tempting because a fraud-detection model's goal is to catch fraud, and perfect recall would achieve that, but that describes a different metric profile.
- ✓
Many fraudulent transactions go undetected
Why this is correct
Recall measures the proportion of actual fraud cases the model identifies. Low recall means many genuine fraudulent transactions are classified as legitimate, so they pass through undetected and generate direct financial loss, despite precision remaining high.
- ✗
Many legitimate transactions are flagged as fraud
Why it's wrong here
Flagging legitimate transactions as fraud is the false-positive problem, which high precision specifically rules out; low recall instead means missed fraud. It is tempting because fraud models do generate false positives, but that outcome corresponds to low precision, not the high-precision, low-recall profile described here.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.