The correct answer isC. ISO/IEC 22989 and 42001 focus onterminology, risk, and management systems, butdo not specifically address procurement-related concerns with third-party vendors.
From the AIGP Body of Knowledge - Standards Section:
"ISO/IEC 22989 defines terminology and foundational concepts. ISO/IEC 42001 provides a management system standard for AI. They are not procurement-focused documents." Also confirmed in the AI Governance in Practice Report2025:
"These standards help establish common language and risk governance procedures. Procurement governance typically falls under separate frameworks or sector-specific guidance." Thus,procurement governance (Option C)is not a central use case for these standards.

Importers of high-risk AI systems into the EU havespecific responsibilitiesunder the EU AI Act. They arenotthe parties responsible for affixing the CE marking or providing technical documentation-but they must verify that these have been done by the provider.
From theAI Governance in Practice Report2025:
"Importers must verify that the appropriate conformity assessment has been carried out, the technical documentation is available, and the CE marking has been affixed." (p. 34-35) Thus:
* A. Provide technical documentation- done by theprovider.
* B. Affix the CE marking-provider'sresponsibility.
* C. Verify the Declaration of Conformity-importer obligation.
* D. Conduct a DPIA- relevant under data protection laws,not requiredunder the EU AI Act forimporters.

The OECD's Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).

When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model's predictions. Model drift is when the model's performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.

Human-centric AI focuses on improving the human experience by addressing individual needs and enhancing human capabilities. Option D exemplifies this by using virtual assistants to tailor educational content to each student's unique abilities and needs, thereby supporting personalized learning and improving educational outcomes. This use case directly benefits individuals by providing customized assistance and adapting to their learning pace and style, aligning with the principles of human-centric AI.
Reference: AIGP BODY OF KNOWLEDGE, sections on trustworthy AI and human-centric AI principles.