AI0-001 AI Concepts and Foundations Practice Question
A hospital uses an AI system to prioritize patient triage based on vital signs and medical history. During a trial, the system consistently assigns lower urgency to elderly patients with chronic conditions, even when their symptoms suggest high risk. Which approach best addresses this bias?
⚠ Common exam trap
CompTIA often tests the misconception that changing the model architecture (e.g., switching to a decision tree) or manually tweaking feature weights can fix bias, when the real solution lies in auditing and rebalancing the training data.
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
✓
Audit the training data for representation of elderly patients and retrain with balanced data
The bias originates from the training data underrepresenting elderly patients with chronic conditions, causing the model to learn skewed urgency patterns. Auditing the data for representation and retraining with balanced data directly addresses the root cause by ensuring the model learns from a fair distribution of cases, which is a standard bias mitigation technique in AI systems.
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 different dataset from a similar hospital without checking demographics
Why it's wrong here
Swapping datasets without demographic checks risks importing the same or different bias, leaving the elderly under-triage unresolved. A different hospital's data suits expanding coverage or validating generalisation, but only after auditing representation and label distributions against the target population.
- ✗
Manually increase the weight of age-related features in the model
Why it's wrong here
Manually upweighting age features amplifies the spurious correlation rather than removing it, pushing predictions further from clinical risk. Feature weighting suits deliberate emphasis of known predictive signals, but here the bias stems from historical data and proxy features that must be rebalanced.
- ✗
Replace the neural network with a decision tree to simplify decision logic
Why it's wrong here
Swapping the neural network for a decision tree changes the model class, not the training data or labels encoding age-related assumptions, so the learned disparity persists. Decision trees suit scenarios needing interpretable, rule-based logic over tabular data, but bias mitigation requires auditing and rebalancing the dataset or applying fairness constraints.
- ✓
Audit the training data for representation of elderly patients and retrain with balanced data
Why this is correct
Auditing the training data for representation of elderly patients directly addresses the dataset bias causing the system to deprioritise this group. Retraining with balanced data corrects the skewed distribution of chronic-condition cases, satisfying the fairness constraint that the AI must not systematically discriminate based on age. This approach ensures the model learns genuine risk patterns rather than spurious correlations from under-represented subgroups.
About these practice questions
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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.