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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance of AI Development | 21โ25% | - Requirements gathering, risk assessment and mitigation - Model management, version control and quality assurance - Governance through design and lifecycle management - Testing, validation, documentation and transparency |
| Topic 2: Foundational Concepts of AI Governance | 24โ28% | - Impacts, risks and benefits of AI systems - Governance frameworks and core concepts - Fundamental AI definitions, types and characteristics - Principles of responsible, ethical and trustworthy AI |
| Topic 3: Governance of AI Deployment and Use | 21โ25% | - Lifecycle maintenance, decommissioning and updates - Selection, procurement and third-party management - Incident response, accountability and audit - Implementation, monitoring and ongoing oversight |
| Topic 4: Laws, Regulations, Standards and Frameworks | 24โ28% | - Alignment with data protection and privacy laws - International standards (ISO 42001, NIST AI RMF, etc.) - Global and regional regulations (EU AI Act, US, etc.) - Sector-specific requirements and compliance obligations |
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NEW QUESTION # 215
Which of the following compliance related controls within an organization is most easily adapted to identify AI risks?
Answer: D
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 # 216
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible AI?
Answer: B
Explanation:
Employing a new software platform is a technology solution rather than a foundational compliance strategy step; organizations typically first assess existing programs, ethics, and stakeholder concerns before selecting tools.
NEW QUESTION # 217
CASE STUDY
Please use the following answer the next question:
Good Values Corporation (GVC) is a U.S. educational services provider that employs teachers to create and deliver enrichment courses for high school students. GVC has learned that many of its teacher employees are using generative Al to create the enrichment courses, and that many of the students are using generative Al to complete their assignments.
In particular, GVC has learned that the teachers they employ used open source large language models ("LLM") to develop an online tool that customizes study questions for individual students. GVC has also discovered that an art teacher has expressly incorporated the use of generative Al into the curriculum to enable students to use prompts to create digital art.
GVC has started to investigate these practices and develop a process to monitor any use of generative Al, including by teachers and students, going forward.
All of the following may be copyright risks from teachers using generative Al to create course content EXCEPT?
Answer: D
Explanation:
All of the options listed may pose copyright risks when teachers use generative AI to create course content, except for students must expressly consent to this use of generative AI. While obtaining student consent is essential for ethical and privacy reasons, it does not directly relate to copyright risks associated with the creation and use of AI-generated content.
Reference: The AIGP Body of Knowledge discusses the importance of addressing intellectual property (IP) risks when using AI-generated content. Copyright risks are typically associated with the use of third-party data and the lack of attribution, rather than the consent of users.
NEW QUESTION # 218
Retrieval-Augmented Generation (RAG) is defined as?
Answer: C
Explanation:
Retrieval-Augmented Generation (RAG)enhances Large Language Models (LLMs) by integratingexternal, up-to-date, or proprietary informationinto the generation pipeline-allowing the model tofetch relevant factsfrom a trusted knowledge source at query time.
Though RAG is not defined directly in the IAPP documents, it is a widely recognized technique in AI governance for ensuringmore accurate and contextually grounded outputs, especially inregulated or high- stakes environmentswhere hallucinations are a concern.
* B, C, and Ddescribe optimization or bias mitigation-not the core function of RAG.
NEW QUESTION # 219
Decreasing the complexity of a machine learning model reduces variance and?
Answer: C
Explanation:
The correct answer is A because of the fundamental bias-variance tradeoff in machine learning. When model complexity is reduced, the model becomes simpler and less flexible, which decreases variance because it is less sensitive to fluctuations in the training data. However, this simplification comes at the cost of increased bias, meaning the model may oversimplify relationships and fail to capture underlying patterns accurately. This tradeoff is a core concept in AI fundamentals and directly impacts model performance, reliability, and governance decisions. From an AI governance perspective, understanding this balance is critical when evaluating model risk, as high bias can lead to systematic errors and fairness issues, while high variance can result in instability and unpredictability in outputs. Proper model tuning aims to balance both for optimal and responsible performance.
NEW QUESTION # 220
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