AI0-001 AI Implementation and Operations Practice Question
A financial institution uses a machine learning model to approve personal loans. The model was trained on historical data that includes applicant age, income, credit score, and loan amount. Compliance officers have received customer complaints suggesting the model may be discriminating against applicants over 60 years old. Initial analysis shows that the approval rate for applicants over 60 is 20 percentage points lower than for younger applicants with similar credit profiles. The data science team has been asked to investigate and remediate any bias. They have access to the training data, model coefficients, and can retrain or modify the model. What is the FIRST step the team should take?
⚠ Common exam trap
CompTIA often tests the misconception that removing a protected attribute (like age) is sufficient to eliminate bias, when in fact proxy features can perpetuate discrimination, making a fairness audit the mandatory first 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
✓
Conduct a fairness audit using appropriate metrics such as disparate impact ratio on the current model.
The first step in addressing potential bias is to conduct a fairness audit using established metrics like the disparate impact ratio (e.g., the 80% rule from the US Equal Employment Opportunity Commission). This quantifies whether the model's approval rate for applicants over 60 is less than 80% of the rate for the younger group, providing a legally and technically sound baseline before any remediation. Without this measurement, any subsequent changes (like resampling or removing features) could be misguided or ineffective.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace the model with a third-party vendor model that claims to be bias-free.
Why it's wrong here
Switching vendors does not ensure fairness and may introduce new issues; audit first.
- ✗
Re-sample the training data to have equal numbers of applicants over and under 60.
Why it's wrong here
Re-sampling without bias assessment may not address the root cause and could hurt model performance.
- ✓
Conduct a fairness audit using appropriate metrics such as disparate impact ratio on the current model.
Why this is correct
An audit quantifies bias and provides a baseline to measure remediation effectiveness.
- ✗
Remove the age feature from the training data and retrain the model.
Why it's wrong here
Other features may correlate with age, so removal alone may not eliminate bias; also lacks measurement.
About these practice questions
This AI0-001 question is part of Courseiva's 754-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.