Amazon Web Services AWS Certified AI Practitioner Exam AIF-C01 Question # 59 Topic 6 Discussion
AIF-C01 Exam Topic 6 Question 59 Discussion:
Question #: 59
Topic #: 6
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.
A.
Use a rule-based system instead of an ML model.
B.
Apply explainable AI techniques to show customers which factors influenced the model ' s decision.
C.
Develop an interactive UI for customers and provide clear technical explanations about the system.
D.
Increase the accuracy of the model to reduce the need for transparency.
The correct answer is B because explainable AI (XAI) provides transparency into how models reach specific decisions. According to AWS documentation, techniques such as SHAP values (SHapley Additive exPlanations) or LIME can identify which input features (e.g., income, debt ratio, or credit history) most influenced a model’s prediction. This helps financial institutions comply with fairness and transparency requirements under regulatory frameworks like the Equal Credit Opportunity Act. AWS SageMaker Clarify is a built-in service that offers explainability reports and bias detection to enhance trust. Rule-based systems and UIs alone do not satisfy transparency standards, and accuracy improvements do not replace explainability. By implementing explainable AI, customers can understand and trust credit limit decisions, reducing bias concerns and ensuring compliance.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation – Explainability and Feature Attribution
AWS Responsible AI Practices – Transparency and Accountability
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