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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Laws, Regulations, Standards and Frameworks | 24–28% | - Global and regional regulations (EU AI Act, US, etc.) - International standards (ISO 42001, NIST AI RMF, etc.) - Alignment with data protection and privacy laws - Sector-specific requirements and compliance obligations |
| Topic 2: Governance of AI Deployment and Use | 21–25% | - Implementation, monitoring and ongoing oversight - Lifecycle maintenance, decommissioning and updates - Incident response, accountability and audit - Selection, procurement and third-party management |
| Topic 3: Foundational Concepts of AI Governance | 24–28% | - Principles of responsible, ethical and trustworthy AI - Governance frameworks and core concepts - Impacts, risks and benefits of AI systems - Fundamental AI definitions, types and characteristics |
| Topic 4: Governance of AI Development | 21–25% | - Model management, version control and quality assurance - Testing, validation, documentation and transparency - Requirements gathering, risk assessment and mitigation - Governance through design and lifecycle management |
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NEW QUESTION # 180
When should an ethical impact assessment for AI be performed?
Answer: D
Explanation:
Ethical impact assessment should begin early and remain iterative throughout the AI lifecycle, making D correct. Waiting until production, a user threshold, or an actual ethical incident would make the assessment reactive rather than preventive. UNESCO describes ethical impact assessment as an " ethics by design " mechanism covering research, design, development, deployment, use, maintenance, monitoring, and eventual termination, while emphasizing its particular value during early research and design. OECD-listed assessment guidance similarly states that the first assessment should occur during design, with reassessments at development, deployment, and other significant lifecycle stages. NIST ' s AI RMF Playbook also recommends impact assessment at key stages because risks, operating conditions, stakeholders, data, and system capabilities can change over time. Continuous reassessment therefore supports proactive identification and mitigation of emerging harms.
NEW QUESTION # 181
The most important factor in ensuring fairness when training an AI system is:
Answer: A
Explanation:
Ensuring fairness largely depends on the attributes and variability of the training data, as diverse and representative data reduces bias and promotes equitable outcomes.
NEW QUESTION # 182
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant Agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the Al solution?
Answer: B
Explanation:
Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.
NEW QUESTION # 183
A US-based mortgage lender has purchased a chatbot. They plan to have the chatbot collect information from consumers who are interested in loans and offer the consumers 2-3 different options based on its current pricing and product offerings, which change frequently. This chatbot was initially developed and previously deployed by a Russian airline for booking flights.
The best option for the part of the process that generates the loan offers is?
Answer: D
NEW QUESTION # 184
What is the most important factor when deciding whether or not to select a proprietary AI model?
Answer: D
Explanation:
Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.
From theAI Governance in Practice Report 2025:
"AI governance starts with defining the corporate strategy for AI... and aligning systems with business purpose and operational context." (p. 11) B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.
NEW QUESTION # 185
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