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| Section | Weight | Objectives |
|---|---|---|
| Governance of AI Development | 21–25% | - Governance through design and lifecycle management - Testing, validation, documentation and transparency - Model management, version control and quality assurance - Requirements gathering, risk assessment and mitigation |
| Laws, Regulations, Standards and Frameworks | 24–28% | - Sector-specific requirements and compliance obligations - International standards (ISO 42001, NIST AI RMF, etc.) - Global and regional regulations (EU AI Act, US, etc.) - Alignment with data protection and privacy laws |
| Governance of AI Deployment and Use | 21–25% | - Selection, procurement and third-party management - Lifecycle maintenance, decommissioning and updates - Implementation, monitoring and ongoing oversight - Incident response, accountability and audit |
| Foundational Concepts of AI Governance | 24–28% | - Principles of responsible, ethical and trustworthy AI - Fundamental AI definitions, types and characteristics - Impacts, risks and benefits of AI systems - Governance frameworks and core concepts |
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NEW QUESTION # 29
All of the following analyses are part of a deactivating risk strategy EXCEPT?
Answer: C
Explanation:
The correct answer is C because understanding algorithmic methodology is primarily a design, development, and model evaluation activity rather than part of a deactivation or sunset risk strategy. A deactivating risk strategy focuses on safely retiring or discontinuing an AI system, ensuring that operational, legal, and business risks are properly managed. This includes evaluating regulatory requirements such as data retention and deletion obligations, assessing business continuity and financial implications, and understanding dependencies with upstream and downstream systems to avoid disruption. These steps ensure a controlled and compliant system shutdown. In contrast, analyzing algorithmic methodology is relevant during system development, validation, and explainability efforts, not during the decommissioning phase of the AI lifecycle.
NEW QUESTION # 30
All of the following analyses are part of a deactivating risk strategy EXCEPT:
Answer: B
Explanation:
Understanding algorithmic methodology is part of assessing model design and functionality, not typically a focus of deactivating risk strategies, which center on operational, regulatory, and system continuity risks.
NEW QUESTION # 31
Scenario:
A global organization wants to align with international frameworks on AI governance. They are reviewing guidance from the OECD on how to incorporate broader governance tools into their AI program.
Codes of conduct and collective agreements are what type of assessment tools as defined by the Organization for Economic Cooperation and Development (OECD)?
Answer: A
Explanation:
The correct answer is B - Procedural. The OECD Framework for Classifying AI Systems categorizes codes of conduct and collective agreements as procedural tools because they guide internal governance and decision-making processes.
From the AIGP ILT Participant Guide - Global Governance Models:
"Procedural tools include internal codes of conduct, collective agreements, and procedural audits that guide governance without necessarily involving technical measurement." AI Governance in Practice Report 2024 elaborates:
"These procedural tools support internal accountability mechanisms and ethics compliance frameworks...
they are part of soft governance."
These tools do not measure or analyze technical performance, hence they are not technical or analytic.
NEW QUESTION # 32
What is the best reason for a company to adopt a policy that prohibits the use of generative AI?
Answer: D
Explanation:
The correct answer is D because one of the most significant risks associated with generative AI is the potential leakage of sensitive, confidential, or proprietary information. AI governance frameworks emphasize data protection, confidentiality, and secure data handling as critical organizational responsibilities. When employees input sensitive data into generative AI tools, especially external or third-party systems, that information may be stored, reused, or exposed unintentionally. This creates legal, regulatory, and reputational risks. As a result, organizations may restrict or prohibit generative AI use to prevent unauthorized data disclosure. The other options relate to operational or business considerations, which are not primary governance drivers. Protecting sensitive information aligns with core AI governance principles such as privacy, security, and risk mitigation, making it the strongest justification for such a policy.
NEW QUESTION # 33
A US-based mortgage lender has purchased a chatbot. They plan to have the chatbot collect information from consumers who are interested in loans and offer the consumers 2-3 different options based on its current pricing and product offerings, which change frequently. This chatbot was initially developed and previously deployed by a Russian airline for booking flights.
The best option for the part of the process that generates the loan offers is?
Answer: A
Explanation:
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.
NEW QUESTION # 34
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