AI0-001 AI Concepts and Foundations Practice Question
A hospital's AI governance committee is reviewing a diagnostic model that performs well on the general population but poorly on a rare disease subgroup. The committee wants to determine whether the model's poor performance on this subgroup is due to a data problem or a model problem. Which action should the committee take FIRST to make this determination?
⚠ Common exam trap
The trap here is assuming that improving overall accuracy or model complexity will automatically fix subgroup performance without first checking data representation.
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
✓
Analyze the distribution of the rare disease subgroup in the training data and compare it to the model's error rates on that subgroup.
The correct action is to analyze subgroup representation and error rates because it directly tests whether the poor performance stems from insufficient or unrepresentative training data. If the subgroup is rare in the data, the model may not learn its patterns; if the subgroup is well represented but errors remain high, the issue may be model-related. This diagnostic step guides subsequent fixes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Analyze the distribution of the rare disease subgroup in the training data and compare it to the model's error rates on that subgroup.
Why this is correct
This action directly investigates whether the subgroup is underrepresented in the training data and whether errors are concentrated there. By comparing subgroup prevalence to error rates, the committee can identify data imbalance or bias as a root cause. It is the most informative first step before considering model architecture or hyperparameter changes.
- ✗
Replace the model with a more complex neural network architecture that can capture more intricate patterns.
Why it's wrong here
A more complex model may overfit the majority class and further degrade rare subgroup performance. Without first understanding whether the data adequately represents the subgroup, architecture changes are premature. The committee should diagnose the data and error patterns before increasing model capacity.
- ✗
Retrain the model on the full dataset with a higher learning rate to improve overall accuracy.
Why it's wrong here
Increasing the learning rate does not diagnose whether the issue is data or model related; it may even cause the model to diverge. This action skips the necessary diagnostic step and could worsen performance on the rare subgroup. The committee needs to inspect the data distribution and model errors before changing training hyperparameters.
- ✗
Deploy the model only for the general population and exclude the rare disease subgroup from its use.
Why it's wrong here
Excluding the subgroup avoids addressing the underlying problem and may create ethical and legal issues. The committee's goal is to determine the cause, not to avoid it. This action does not help distinguish between data and model problems and could harm patients who need the model most.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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