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| Section | Weight | Objectives |
|---|---|---|
| Foundational Concepts of AI Governance | 24โ28% | - Governance frameworks and core concepts - Principles of responsible, ethical and trustworthy AI - Impacts, risks and benefits of AI systems - Fundamental AI definitions, types and characteristics |
| Laws, Regulations, Standards and Frameworks | 24โ28% | - Alignment with data protection and privacy laws - International standards (ISO 42001, NIST AI RMF, etc.) - Sector-specific requirements and compliance obligations - Global and regional regulations (EU AI Act, US, etc.) |
| Governance of AI Deployment and Use | 21โ25% | - Lifecycle maintenance, decommissioning and updates - Incident response, accountability and audit - Implementation, monitoring and ongoing oversight - Selection, procurement and third-party management |
| Governance of AI Development | 21โ25% | - Requirements gathering, risk assessment and mitigation - Testing, validation, documentation and transparency - Model management, version control and quality assurance - Governance through design and lifecycle management |
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NEW QUESTION # 29
All of the following are required for high-risk AI systems under the EU AI Act EXCEPT?
Answer: A
Explanation:
The EU AI Act imposes severalmandatory obligationson high-risk AI systems, butpublishing a detailed report on training dataisnotone of them.
From theAI Governance in Practice Report 2024:
"It mandates drawing up technical documentation for high-risk AI systems, and requires high-risk AI systems to come with instructions for use that disclose various information, including characteristics, capabilities and performance limitations." (p. 34)
"To make high-risk AI systems more traceable, it also requires AI systems to be able to automatically allow for the maintenance of logs throughout the AI life cycle."
"Conducting post-market monitoring" and "conformity assessments" areexplicitrequirements for high-risk systems. (p. 34-35) However,publishingdetailed training data is typicallyrequired only for general-purpose AI systems with systemic risk, not standard high-risk AI systems.
* A. Log retention,B. Post-market monitoring, andC. Conformity assessmentsareall requiredunder the EU AI Act for high-risk systems.
NEW QUESTION # 30
What is the main purpose of accountability structures under the Govern function of the NIST AI Risk Management Framework?
Answer: C
Explanation:
Accountability structures under the NIST AI Risk Management Framework's Govern function focus on empowering and training cross-functional teams to manage AI risks effectively.
NEW QUESTION # 31
What is a primary purpose of establishing external communication plans in AI governance?
Answer: C
Explanation:
External communication plans primarily establish how an organization communicates relevant AI information, risks, incidents, limitations, deployment changes, and governance matters to people outside the organization. A therefore provides the broadest and most accurate purpose. The current IAPP AIGP Study Guide expressly treats external communications as part of ongoing AI operations and associates them with transparency and trust-building. NIST AI RMF likewise requires communication plans for identified and measured AI risks and encourages communication with external stakeholders, affected communities, users, and other relevant AI actors. B concerns internal training rather than external communication. C belongs to third-party procurement and contract governance. Regulatory notification in D may form one component of an external communication plan when legally required, but it is considerably narrower than the overall governance objective of transparent stakeholder communication represented by A.
NEW QUESTION # 32
During the first month when the company monitors the model for bias, it is most important to?
Answer: D
Explanation:
Theinitial deployment phaseof an AI model is critical forpost-deployment monitoring. When tracking forbias, the most important task is tocontinue disparity testingto determine whether outputs differ across protected groups.
From theAI Governance in Practice Report 2025:
"Performance monitoring protocols... should include mechanisms to assess and measure disparities in outcomes across different demographic groups." (p. 12)
"Bias may not be evident during pre-deployment testing but can emerge in real-world use." (p. 41) B . Awareness trainingis helpful, but not a technical bias mitigation activity.
C . Analyzing training datais apre-deploymenttask.
D . Documenting human decisionsmay support auditability but doesn't detect bias in AI outputs.
NEW QUESTION # 33
Business A sells software that provides users with writing and grammar assistance. Business B is a cloud services provider that trains its own AI models.
* Business A has decided to add generative AI features to their software.
* Rather than create their own generative AI model, Business A has chosen to license a model from Business B:
* Business A will then integrate the model into their writing assistance software to provide generative AI capabilities.
* Business A is most concerned that its writing assistance software could recommend toxic or obscene text to its users.
Which of the following governance processes should Business A take to best protect its users against potentially inappropriate text?
Answer: B
Explanation:
Business A is integrating a generative AI model licensed from a third party (Business B) and is primarily concerned with the risk of toxic or obscene outputs being delivered to users. In this scenario,testing and validationof the AI model for such content risks is the most direct and effective governance strategy.
According to theAI Governance in Practice Report 2024, organizations thatdeployAI must engage in performance monitoring protocolsand ensure systems perform adequately for theirintended purposes, including filtering harmful content:
"Operational governance... development of: #Performance monitoring protocols to ensure systems perform adequately for their intended purposes." (p. 12)
"Product governance... includes: #System impact assessments to identify and address risk prior to product development or deployment." (p. 11) Furthermore, under theEU AI Act, which sets the global standard many organizations aim to align with, there is a clear obligation to test and monitor systems for potential harmful behavior:
"The act imposes regulatory obligations... such as establishing appropriate accountability structures,assessing system impact, providing technical documentation,establishing risk management protocols and monitoring performance..." (p. 7) Option B directly reflects this best practice ofpre-deployment testing and validationto ensure that the model aligns with Business A's minimum content safety requirements.
Let's now evaluate the incorrect options:
* A. Fine-tuning on verified user-generated textmay improve model alignment but does not guarantee that the model will generalize correctly, especially if Business A lacks access to model internals (common in third-party licensing scenarios). Fine-tuning also introduces its own risks and may be contractually restricted.
* C. A user reporting featureisreactive, not preventive. While helpful for long-term monitoring and mitigation, it does not prevent the initial harm of toxic outputs, which isBusiness A's primary concern.
* D. Requesting documentation from Business Bis useful for transparency and risk management, but it does not replaceindependent verificationthat the model meets Business A's content safety standards.
Thus,testing the model's behavior for unacceptable outputs before deploymentis the most aligned approach with AI governance best practices and obligations.
NEW QUESTION # 34
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