AI0-001 Implementing AI Solutions Practice Question
A healthcare provider is deploying an AI model to predict patient readmission risk. The model was trained on historical data that includes a feature indicating whether the patient has diabetes. The provider wants to ensure the model does not discriminate based on this feature. Which technique should be used to detect and mitigate bias related to the diabetes feature?
⚠ Common exam trap
The trap here is assuming that removing a sensitive feature eliminates bias, when proxy features can still cause discrimination.
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
✓
Perform a fairness audit by comparing model performance across groups with and without diabetes.
A fairness audit systematically compares model outcomes across groups defined by the sensitive attribute, revealing disparities. This is the essential first step to detect bias. Once identified, mitigation techniques like reweighting or adversarial debiasing can be applied. Removing the feature or changing metrics does not directly address the need to detect and mitigate bias.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Perform a fairness audit by comparing model performance across groups with and without diabetes.
Why this is correct
A fairness audit evaluates whether the model's predictions are equitable across subgroups defined by the sensitive feature. By comparing metrics like true positive rate or false positive rate between diabetic and non-diabetic patients, the provider can identify disparate impact. This is a standard method to detect bias and informs mitigation strategies such as reweighting or adversarial debiasing.
- ✗
Use a different evaluation metric such as AUC-ROC instead of accuracy to assess model performance.
Why it's wrong here
AUC-ROC measures overall discriminative ability but does not reveal bias across subgroups. A model can have high AUC-ROC yet perform poorly for a specific group. To detect bias related to diabetes, subgroup analysis is necessary. Changing the metric alone does not provide the needed fairness assessment.
- ✗
Remove the diabetes feature from the dataset and retrain the model.
Why it's wrong here
Removing the feature does not guarantee fairness because other correlated features may still encode the same information, leading to proxy discrimination. Moreover, diabetes is a medically relevant factor for readmission risk, so its removal could reduce model accuracy without addressing underlying bias. A fairness audit is needed first to understand the bias before deciding on mitigation.
- ✗
Increase the model's complexity by adding more layers to capture subtle patterns related to diabetes.
Why it's wrong here
Increasing complexity can exacerbate bias by allowing the model to learn spurious correlations, making discrimination worse. It does not detect or mitigate bias; instead, it may obscure it. The goal is to ensure fairness, so adding layers without a fairness evaluation is counterproductive and not a recommended technique.
About these practice questions
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JA
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
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.