Offeringloan products based on current offerings and rulesrequires a system that can followexplicit business logic, not generate open-ended content. Anexpert system, which is a rules-based AI that uses "if- then" logic, is ideal here.
From the AI governance context:
"Rule-based AI systems are often preferred when decisions must adhere to precise regulatory or financial criteria." (aligned with AI best practices in regulated sectors)
* A. RAGis used to integrate external knowledge-not suitable for structured, rule-based logic.
* B. Multimodal modelshandle varied input types-not needed here.
* D. Quantum computingis not yet practical or relevant for this business use case.

The correct answer is D, model disgorgement. This technique refers to removing or eliminating the influence of improperly obtained, biased, or unlawfully used data from a trained machine learning model. It is increasingly discussed in AI governance and regulatory enforcement, particularly where models have been trained on data collected without proper consent or in violation of legal requirements. Instead of merely cleaning datasets, model disgorgement may require retraining or discarding the model entirely to ensure that problematic data no longer influences outputs. This aligns with accountability and compliance principles in AI governance, where organizations must ensure lawful data use throughout the AI lifecycle. Other options like data cleansing and de-duplication address data quality but do not fully remove learned patterns already embedded in trained models.

While risk management is important, the EU AI Act requires documenting the AI system's description, performance metrics appropriateness, and detailed technical elements, but not specifically the prioritization of deployment risks in the technical documentation.

Testing results need to bedocumented thoroughlyto ensuretraceability, accountability, and compliance. This is central to enabling audits, investigations, or regulatory inquiries into the system's development and performance.
From theAI Governance in Practice Report2025:
"Documentation and recordkeeping are essential components... to demonstrate AI system compliance, trace system behavior, and support audits and conformity assessments." (p. 34-35)
"Maintaining audit trails across development and deployment enables transparency and accountability." (p.
12)
* AandBare benefits, but not theprimary governance justification.
* D- Limiting future testing is not a recommended goal.

The correct answeris ExplainableAi becauseit specifically refers tothe abilityof a systemto describethe logic behind its decisions oroutput ina way that is understandable to humans. This is a key part of regulatory and ethical frameworks and is directly related to addressingthe black-boxproblem inAI.
From the AIGP ILT Participant Guide (Module on Transparency and Explainability):
"Explainability refers to the understanding of how a black-box model works. The black-box problem exists because some models are too complex for human interpretation. Explainability methods aim to provide meaningful insight into the logic and decision-making of AI systems." Also, according to the AI Governance in Practice Report2025:
"Explainability refers to the representation of the underlying mechanisms of the AI system's operation... a key tenet of AI governance due to the desire to understand how AI systems are built, managed and maintained." Thus, while Trustworthy and Responsible AI are broader concepts,explainabilityspecifically targetsthe regulatoryconcern about understanding outputs.