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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Governance of AI Development | 21โ25% | - Model management, version control and quality assurance - Testing, validation, documentation and transparency - Governance through design and lifecycle management - Requirements gathering, risk assessment and mitigation |
| Topic 3: Laws, Regulations, Standards and Frameworks | 24โ28% | - International standards (ISO 42001, NIST AI RMF, etc.) - Sector-specific requirements and compliance obligations - Alignment with data protection and privacy laws - Global and regional regulations (EU AI Act, US, etc.) |
| Topic 4: Governance of AI Deployment and Use | 21โ25% | - Implementation, monitoring and ongoing oversight - Incident response, accountability and audit - Selection, procurement and third-party management - Lifecycle maintenance, decommissioning and updates |
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NEW QUESTION # 45
All of the following are elements of establishing a global Al governance infrastructure EXCEPT?
Answer: D
Explanation:
Establishing a global AI governance infrastructure involves several key elements, including providing training to foster a culture that promotes ethical behavior, creating policies and procedures to manage third-party risk, and understanding differences in norms across countries. While publicly disclosing ethical principles can enhance transparency and trust, it is not a core element necessary for the establishment of a governance infrastructure. The focus is more on internal processes and structures rather than public disclosure. Reference:
AIGP Body of Knowledge on AI Governance and Infrastructure.
NEW QUESTION # 46
In procuring an AI system from a vendor, which of the following would be important to include in a contract to enable proper oversight and auditing of the system?
Answer: D
Explanation:
Ensuringoversight and auditabilityrequires that the organization hassufficient access to data, documentation, and model internalsor outputs necessary for evaluation.
From theAI Governance in Practice Report2025:
"Access to technical documentation and system internals is essential to enable effective auditing, conformity checks, and accountability mechanisms." (p. 11, 34)
* Ais about liability, not auditability.
* Bmatters for IP rights, not oversight.
* Crelates to lifecycle responsibility but doesn't guarantee audit access.
NEW QUESTION # 47
A company deploys an AI model for fraud detection in online transactions. During its operation, the model begins to exhibit high rates of false positives, flagging legitimate transactions as fraudulent.
Which is the best step the company should take to address this development?
Answer: B
Explanation:
When an AI system causessignificant false positives, especially in sensitive contexts likefraud detection, the priority is tohalt harmful activityand perform a full assessment. Continued use without understanding the fault may cause furthercustomer harmand legal exposure.
From theAI Governance in Practice Report 2024:
"Incident management plans should enable identification, escalation, and system rollback to prevent continued harm from malfunctioning AI systems." (p. 12, 35)
NEW QUESTION # 48
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?
Answer: A
Explanation:
Autoregression is not a common optimization technique in deep learning to determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with time-series analysis and forecasting rather than neural network optimization.
Reference: AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning.
NEW QUESTION # 49
What is the technique to remove the effects of improperly used data from an ML system?
Answer: D
Explanation:
Model disgorgement is the technique used to remove the influence of improperly used or problematic data from a trained machine learning model.
NEW QUESTION # 50
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