AI0-001 AI Concepts and Foundations Practice Question
A hospital deploys an AI system to detect pneumonia from chest X-rays. The model achieves 95% accuracy on the test set but later is found to be less accurate for patients under 18. The development team suspects bias. Which step should be taken first to investigate?
⚠ Common exam trap
CompTIA often tests the principle that aggregate metrics like overall accuracy can be misleading, and the trap here is that candidates jump to a solution (retraining or adding features) before performing the necessary diagnostic step of subgroup performance analysis.
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
✓
Compute and compare performance metrics for different age subgroups in the test set.
The first step in investigating suspected model bias is to perform a disaggregated analysis of performance metrics across relevant subgroups, such as age brackets. This directly identifies whether the model's accuracy, precision, recall, or other metrics differ significantly for pediatric patients versus adults, confirming the presence and nature of the bias before any remediation is attempted.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Automatically retrain the model with a balanced dataset including more pediatric cases.
Why it's wrong here
Retraining without confirming the cause may not solve the problem and could introduce other issues.
- ✗
Expand the test set with more pediatric X-rays and re-evaluate overall accuracy.
Why it's wrong here
Overall accuracy may still hide subgroup disparities; need stratified analysis.
- ✓
Compute and compare performance metrics for different age subgroups in the test set.
Why this is correct
Subgroup analysis is the standard first step in fairness auditing.
- ✗
Add more features to the model to capture age-related anatomical differences.
Why it's wrong here
Feature engineering might help but is not the first diagnostic step.
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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.