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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance of AI Development | 21–25% | - Governance through design and lifecycle management - Requirements gathering, risk assessment and mitigation - Model management, version control and quality assurance - Testing, validation, documentation and transparency |
| Topic 2: Laws, Regulations, Standards and Frameworks | 24–28% | - Global and regional regulations (EU AI Act, US, etc.) - International standards (ISO 42001, NIST AI RMF, etc.) - Sector-specific requirements and compliance obligations - Alignment with data protection and privacy laws |
| Topic 3: Governance of AI Deployment and Use | 21–25% | - Incident response, accountability and audit - Implementation, monitoring and ongoing oversight - Lifecycle maintenance, decommissioning and updates - Selection, procurement and third-party management |
| Topic 4: Foundational Concepts of AI Governance | 24–28% | - Fundamental AI definitions, types and characteristics - Principles of responsible, ethical and trustworthy AI - Impacts, risks and benefits of AI systems - Governance frameworks and core concepts |
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NEW QUESTION # 127
Which of the following is the least relevant consideration in assessing whether users should be given the right to opt out from an Al system?
Answer: C
Explanation:
When assessing whether users should be given the right to opt out from an AI system, the primary considerations are feasibility, risk to users, and industry practice. Feasibility addresses whether the opt-out mechanism can be practically implemented. Risk to users assesses the potential harm or benefits users might face if they cannot opt out. Industry practice considers the norms and standards within the industry. However, the cost of alternative mechanisms, while important in the broader context of implementation, is not directly relevant to the ethical consideration of whether users should have the right to opt out. The focus should be on protecting user rights and ensuring ethical AI practices.
Reference: AIGP BODY OF KNOWLEDGE, sections discussing user rights and ethical considerations in AI.
NEW QUESTION # 128
What is the technique to remove the effects of improperly used data from an ML system?
Answer: B
Explanation:
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.
NEW QUESTION # 129
Which of the following compliance related controls within an organization is most easily adapted to identify AI risks?
Answer: B
Explanation:
The correct answer is D because Privacy Impact Assessments are already structured processes designed to identify risks related to data use, processing, and potential harm to individuals. These assessments can be readily adapted to evaluate AI-specific risks, such as bias, automated decision-making impacts, and data protection concerns. AI governance frameworks emphasize leveraging existing compliance mechanisms to efficiently integrate AI risk management without duplicating processes. Privacy impact assessments align closely with AI risk evaluation because they examine how personal data is collected, used, and protected throughout the system lifecycle. Other options, such as penetration testing or training, focus on narrower objectives like security or awareness and are not comprehensive tools for identifying broader AI risks.
Adapting PIAs supports a risk-based, scalable, and governance-aligned approach to managing AI systems.
NEW QUESTION # 130
Testing data is defined as a subset of data that is used to:
Answer: B
Explanation:
Testing data is used to provide an unbiased evaluation of the final model's performance before deployment.
NEW QUESTION # 131
An AI start-up is developing a system for automated loan approvals. The team wants to minimize risks of bias and regulatory non-compliance. They have already identified potential stakeholders, including regulators and consumer groups.
What is the most appropriate sequence of next steps?
Answer: C
Explanation:
The correct answer is C because it follows a structured, risk-based AI governance approach aligned with best practices and regulatory expectations. AI governance frameworks emphasize identifying and assessing risks through probability and severity analysis before deployment. This allows organizations to prioritize the most critical risks, such as bias and discrimination in loan approvals. Applying a risk mitigation hierarchy ensures that controls are implemented proactively rather than reactively. Only after risks are assessed and mitigated should pilot testing occur to validate system performance in a controlled environment. Other options either delay risk assessment until after deployment or focus on limited aspects like benchmarking or explainability without addressing core risk management principles. A proactive, risk-first approach is essential for compliance, fairness, and responsible AI deployment.
NEW QUESTION # 132
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