- A
Apply a fairness constraint that penalizes the model for disparate impact
Why wrong: Fairness constraints can be effective but are complex; resampling is simpler and more direct.
- B
Discontinue the AI model and use manual approval for all loans
Why wrong: Manual approval may be less efficient and still biased.
- C
Resample the training data to ensure balanced representation of ethnicities
Resampling addresses the root cause by balancing training data.
- D
Adjust the approval threshold so that approval rates are equal across ethnic groups
Why wrong: This could lead to unqualified approvals or rejections without fixing the model.
Question 361 of 1,000
AI0-001 AI Security, Ethics and Governance Practice Question
This AI0-001 practice question tests your understanding of ai security, ethics and governance. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A credit union uses an AI model to approve personal loans. The model was trained on historical data from the past five years. A recent internal review shows that the model approves loans predominantly for white applicants compared to other ethnicities, even when income and credit scores are similar. The credit union wants to comply with fair lending laws without significantly reducing overall approval rates. The data science team has access to the training data. What is the most appropriate remediation step?
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
✓
Resample the training data to ensure balanced representation of ethnicities
Option C is correct because resampling the training data to ensure balanced representation of ethnicities directly addresses the root cause of the bias—skewed historical data—without altering the model's decision logic or approval thresholds. By rebalancing the dataset (e.g., oversampling underrepresented groups or undersampling the majority), the model learns from a more equitable distribution of features, reducing disparate impact while preserving overall approval rates. This approach aligns with fair lending laws by mitigating bias at the data level, which is the most fundamental and effective remediation step.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply a fairness constraint that penalizes the model for disparate impact
Why it's wrong here
Fairness constraints can be effective but are complex; resampling is simpler and more direct.
- ✗
Discontinue the AI model and use manual approval for all loans
Why it's wrong here
Manual approval may be less efficient and still biased.
- ✓
Resample the training data to ensure balanced representation of ethnicities
Why this is correct
Resampling addresses the root cause by balancing training data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Adjust the approval threshold so that approval rates are equal across ethnic groups
Why it's wrong here
This could lead to unqualified approvals or rejections without fixing the model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that adjusting the approval threshold (Option D) is a valid fairness intervention, but the trap here is that threshold adjustment only changes the cutoff for decisions without fixing the underlying biased feature representations, leading to inconsistent and potentially illegal outcomes under fair lending laws.
Detailed technical explanation
How to think about this question
Resampling techniques such as SMOTE (Synthetic Minority Oversampling Technique) or random undersampling can be applied to the training data to correct for imbalanced representation of ethnic groups. Under the hood, this changes the prior distribution the model learns, reducing the influence of majority-group patterns that dominate in biased historical data. In a real-world scenario, a credit union might use stratified resampling to ensure that each ethnic group contributes equally to the model's gradient updates during training, thereby reducing disparate impact without needing to alter the model architecture or post-processing thresholds.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
AI Security, Ethics and Governance — study guide chapter
Learn the concepts, then practise the questions
- →
AI Security, Ethics and Governance practice questions
Targeted practice on this topic area only
- →
All AI0-001 questions
1,000 questions across all exam domains
- →
CompTIA AI+ AI0-001 study guide
Full concept coverage aligned to exam objectives
- →
AI0-001 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI0-001 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
AI Infrastructure and Technologies practice questions
Practise AI0-001 questions linked to AI Infrastructure and Technologies.
AI Security practice questions
Practise AI0-001 questions linked to AI Security.
AI Concepts and Foundations practice questions
Practise AI0-001 questions linked to AI Concepts and Foundations.
AI Concepts and Techniques practice questions
Practise AI0-001 questions linked to AI Concepts and Techniques.
Machine Learning and Deep Learning practice questions
Practise AI0-001 questions linked to Machine Learning and Deep Learning.
AI Models and Data Engineering practice questions
Practise AI0-001 questions linked to AI Models and Data Engineering.
Implementing AI Solutions practice questions
Practise AI0-001 questions linked to Implementing AI Solutions.
AI Implementation and Operations practice questions
Practise AI0-001 questions linked to AI Implementation and Operations.
AI Security, Ethics and Governance practice questions
Practise AI0-001 questions linked to AI Security, Ethics and Governance.
AI Governance and Ethics practice questions
Practise AI0-001 questions linked to AI Governance and Ethics.
CompTIA A+ hardware practice questions
Practise AI0-001 questions linked to CompTIA A+ hardware.
CompTIA A+ mobile devices practice questions
Practise AI0-001 questions linked to CompTIA A+ mobile devices.
Practice this exam
Start a free AI0-001 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Resample the training data to ensure balanced representation of ethnicities — Option C is correct because resampling the training data to ensure balanced representation of ethnicities directly addresses the root cause of the bias—skewed historical data—without altering the model's decision logic or approval thresholds. By rebalancing the dataset (e.g., oversampling underrepresented groups or undersampling the majority), the model learns from a more equitable distribution of features, reducing disparate impact while preserving overall approval rates. This approach aligns with fair lending laws by mitigating bias at the data level, which is the most fundamental and effective remediation step.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More AI0-001 practice questions
- A data science team uses a CI/CD pipeline for ML models. They need to ensure that each model version is traceable back t…
- A company is deploying a large language model for customer support. They want to reduce the number of off-topic or nonse…
- A data scientist fine-tunes a large language model for a legal document summarization task. After fine-tuning, the model…
- A team is designing an AI system for autonomous driving. They need to decide between an end-to-end deep learning approac…
- A team is using Kubeflow to orchestrate ML workflows on Kubernetes. They need to ensure reproducibility, track experimen…
- A healthcare organization deploys an AI system to analyze medical images and detect anomalies. During a routine audit, t…
Last reviewed: Jul 4, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.