AIF-C01 Guidelines for Responsible AI Practice Question
A company uses an AI system to screen job applications. The system was trained on resumes from previous hires, which predominantly came from a specific demographic. As a result, the system may unfairly filter out qualified candidates from other backgrounds. Which responsible AI practice should the company implement?
⚠ Common exam trap
AWS often tests the misconception that improving model accuracy or simply adding more data automatically fixes bias, when in fact biased training data requires targeted fairness interventions like reweighting, resampling, or adversarial debiasing.
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
✓
Implement bias detection metrics and monitor outcomes by demographic groups
Implementing bias detection metrics and monitoring outcomes by demographic groups directly addresses the risk of unfair filtering. This practice aligns with the responsible AI principle of fairness, requiring continuous evaluation of model outputs across protected groups to identify and mitigate demographic disparities in hiring decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement bias detection metrics and monitor outcomes by demographic groups
Why this is correct
Training data skewed toward one demographic produces disparate impact, so measuring outcomes across demographic groups and applying bias detection metrics exposes and quantifies that skew. This satisfies the stem's requirement to address unfair filtering of qualified candidates from other backgrounds.
- ✗
Focus solely on improving the model's precision and recall
Why it's wrong here
Precision and recall measure overall predictive accuracy, not demographic fairness, so a biased model can score well on both. It is tempting because tuning metrics improves performance, and that is correct for general model quality, but the stem requires mitigating disparate impact across demographic groups.
- ✗
Defer all screening decisions to a human recruiter
Why it's wrong here
Human review alone does not remove the bias encoded in the model's recommendations, and it scales poorly across application volumes. It is tempting because human oversight is a recognised responsible AI control, and it is correct for high-stakes exceptions, but the stem requires correcting the training data imbalance itself.
- ✗
Increase the size of the training dataset without regard to demographic composition
Why it's wrong here
Larger volumes of the same biased historical data entrench the demographic skew rather than correcting it, because the label distribution itself is skewed. It is tempting as a generic fix for underfitting, and would be right if the model were merely data-starved rather than reflecting sampling bias.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.