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| Section | Weight | Objectives |
|---|---|---|
| Laws, Regulations, Standards and Frameworks | 24–28% | - International standards (ISO 42001, NIST AI RMF, etc.) - Global and regional regulations (EU AI Act, US, etc.) - Sector-specific requirements and compliance obligations - Alignment with data protection and privacy laws |
| Foundational Concepts of AI Governance | 24–28% | - Impacts, risks and benefits of AI systems - Principles of responsible, ethical and trustworthy AI - Fundamental AI definitions, types and characteristics - Governance frameworks and core concepts |
| Governance of AI Development | 21–25% | - Governance through design and lifecycle management - Model management, version control and quality assurance - Requirements gathering, risk assessment and mitigation - Testing, validation, documentation and transparency |
| Governance of AI Deployment and Use | 21–25% | - 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 # 59
Which of the following Al uses is best described as human-centric?
Answer: B
Explanation:
Human-centric AI focuses on improving the human experience by addressing individual needs and enhancing human capabilities. Option D exemplifies this by using virtual assistants to tailor educational content to each student's unique abilities and needs, thereby supporting personalized learning and improving educational outcomes. This use case directly benefits individuals by providing customized assistance and adapting to their learning pace and style, aligning with the principles of human-centric AI.
Reference: AIGP BODY OF KNOWLEDGE, sections on trustworthy AI and human-centric AI principles.
NEW QUESTION # 60
CASE STUDY
Please use the following answer the next question:
A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public sites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.
During the procurement process, what is the most likely reason that the third-party consultant asked each vendor for information about the diversity of their datasets?
Answer: D
Explanation:
The third-party consultant asked each vendor for information about the diversity of their datasets to assist in ensuring the fairness of the AI system. Diverse datasets help prevent biases and ensure that the AI system performs equitably across different demographic groups. This is crucial for a law enforcement application, where fairness and avoiding discriminatory practices are of paramount importance. Ensuring diversity in training data helps in building a more just and unbiased AI system. Reference: AIGP Body of Knowledge on Ethical AI and Fairness.
NEW QUESTION # 61
Scenario:
A global organization wants to align with international frameworks on AI governance. They are reviewing guidance from the OECD on how to incorporate broader governance tools into their AI program.
Codes of conductandcollective agreementsare what type of assessment tools as defined by theOrganization for Economic Cooperation and Development (OECD)?
Answer: B
Explanation:
The correct answer isB - Procedural. TheOECD Framework for Classifying AI Systemscategorizescodes of conduct and collective agreementsasprocedural toolsbecause they guide internal governance and decision- making processes.
From the AIGP ILT Participant Guide - Global Governance Models:
"Procedural tools include internal codes of conduct, collective agreements, and procedural audits that guide governance without necessarily involving technical measurement." AI Governance in Practice Report2025elaborates:
"These procedural tools support internal accountability mechanisms and ethics compliance frameworks...
they are part of soft governance."
These toolsdo not measure or analyze technical performance, hence they arenot technical or analytic.
NEW QUESTION # 62
What is the best method to apply to an AI model so that it is statistically unlikely that a specific piece of training data can be identified from a model ' s output?
Answer: A
Explanation:
The correct answer is C because differential privacy is specifically designed to ensure that the inclusion or exclusion of any single data point does not significantly affect the output of a model. This provides strong mathematical guarantees that individual training data cannot be reverse-engineered or identified from model outputs. Differential privacy works by introducing controlled noise into the data or model outputs, thereby protecting individual-level information while preserving overall statistical patterns. In AI governance, it is widely recognized as a key privacy-enhancing technology for mitigating risks such as data leakage, membership inference attacks, and re-identification. Other options, such as homomorphic encryption, focus on secure computation, while data sharding and compartmentalization address data management but do not provide formal privacy guarantees at the model output level.
NEW QUESTION # 63
What is the primary purpose of an AI impact assessment?
Answer: B
Explanation:
The primary purpose of an AI impact assessment is to proactively anticipate, identify, and manage potential risks and harms associated with the deployment of an AI system.
NEW QUESTION # 64
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