AI0-001 AI Security, Ethics and Governance Practice Question
A large hospital system deploys an AI triage system for emergency rooms. The system uses patient vitals and symptoms to recommend treatment priority. Six months after deployment, complaints arise that the system frequently underestimates the severity of symptoms for patients from certain ethnic backgrounds. A data scientist runs a bias audit and finds that the model's false negative rate is 20% higher for the minority group. The hospital's AI governance board requires immediate corrective action. The data science team has limited resources and cannot retrain the entire model from scratch. They have access to the training data, which is imbalanced. The model is a gradient boosted tree. Which course of action best addresses the bias while minimizing operational impact?
⚠ Common exam trap
CompTIA often tests the misconception that bias mitigation always requires retraining or complex algorithmic changes, when in fact post-processing threshold adjustments can be a quick, effective fix for deployed models with limited resources.
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
✓
Post-process the model's predictions by adjusting thresholds for the minority group
Post-processing by adjusting decision thresholds for the minority group directly compensates for the higher false negative rate without requiring retraining. Since the team has limited resources and cannot retrain the entire gradient boosted tree model, this approach minimizes operational impact while addressing the bias. The threshold adjustment effectively lowers the probability cutoff for the minority group, making the model more sensitive to their symptoms and reducing underestimation of severity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Rebalance the training data using SMOTE and retrain the model
Why it's wrong here
SMOTE retraining requires time and may not fully resolve bias if the model still learns proxy features.
- ✗
Use adversarial debiasing during training to remove protected attribute correlations
Why it's wrong here
Adversarial debiasing requires retraining, which contradicts the limited resources constraint.
- ✓
Post-process the model's predictions by adjusting thresholds for the minority group
Why this is correct
Threshold adjustment is fast, cheap, and directly minimizes false negative disparity.
- ✗
Replace the model with a simpler logistic regression model to improve interpretability
Why it's wrong here
Switching models is a major change and may degrade overall performance.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 754 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.