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| Section | Weight | Objectives |
|---|---|---|
| Governance of AI Deployment and Use | 21–25% | - Implementation, monitoring and ongoing oversight - Selection, procurement and third-party management - Lifecycle maintenance, decommissioning and updates - Incident response, accountability and audit |
| Foundational Concepts of AI Governance | 24–28% | - Impacts, risks and benefits of AI systems - Fundamental AI definitions, types and characteristics - Governance frameworks and core concepts - Principles of responsible, ethical and trustworthy AI |
| Laws, Regulations, Standards and Frameworks | 24–28% | - Alignment with data protection and privacy laws - Global and regional regulations (EU AI Act, US, etc.) - Sector-specific requirements and compliance obligations - International standards (ISO 42001, NIST AI RMF, etc.) |
| Governance of AI Development | 21–25% | - Requirements gathering, risk assessment and mitigation - Governance through design and lifecycle management - Testing, validation, documentation and transparency - Model management, version control and quality assurance |
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NEW QUESTION # 39
The OECD's Ethical AI Governance Framework is a self-regulation model that proposes to prevent societal harms by:
Answer: A
Explanation:
The OECD's Ethical AI Governance Framework emphasizes balancing AI innovation with ethical considerations to prevent societal harms while fostering technological progress.
NEW QUESTION # 40
All of the following may be copyright risks from teachers using generative AI to create course content EXCEPT?
Answer: D
Explanation:
The correct answer is C. This option concerns consent, which is generally a privacy, transparency, or institutional governance issue rather than a copyright issue. Copyright risk in AI governance usually relates to whether protected third party works were used in model training, whether generated outputs may reproduce copyrighted expression, and whether content is created without proper attribution or clear ownership boundaries. That makes options A, B, and D directly relevant to copyright and intellectual property concerns.
In contrast, whether students must expressly consent deals with notice, fairness, and lawful or ethical use in an educational environment. For AIGP exam purposes, it is important to separate intellectual property risks from privacy and data protection obligations, even when both appear in the same classroom or organizational AI use case.
NEW QUESTION # 41
A company subject to GDPR is building its governance framework for how it will collect data to be used for training of AI models. The most important thing the company can do to ensure GDPR compliance is:
Answer: A
Explanation:
Minimizing the amount of personal data used in training supports GDPR's data minimization principle, reducing privacy risks and improving compliance.
NEW QUESTION # 42
Decreasing the complexity of a machine learning model reduces variance and?
Answer: A
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 per formance, 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 # 43
Random forest algorithms are in what type of machine learning model?
Answer: A
Explanation:
Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms. They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.
NEW QUESTION # 44
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