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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance of AI Deployment and Use | 21–25% | - Incident response, accountability and audit - Selection, procurement and third-party management - Implementation, monitoring and ongoing oversight - Lifecycle maintenance, decommissioning and updates |
| Topic 2: Foundational Concepts of AI Governance | 24–28% | - Governance frameworks and core concepts - Fundamental AI definitions, types and characteristics - Impacts, risks and benefits of AI systems - Principles of responsible, ethical and trustworthy AI |
| Topic 3: Governance of AI Development | 21–25% | - Requirements gathering, risk assessment and mitigation - Testing, validation, documentation and transparency - Model management, version control and quality assurance - Governance through design and lifecycle management |
| Topic 4: Laws, Regulations, Standards and Frameworks | 24–28% | - International standards (ISO 42001, NIST AI RMF, etc.) - Sector-specific requirements and compliance obligations - Global and regional regulations (EU AI Act, US, etc.) - Alignment with data protection and privacy laws |
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NEW QUESTION # 20
CASE STUDY
Please use the following to answer the next question:
A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to 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 to a radiologist for secondary review pursuant to agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has:
- Defined its AI ethical principles.
- Conducted discovery to identify the intended uses and success
criteria for the system.
- Established an AI risk committee.
- Assembled a cross-functional team with clear roles and
responsibilities.
- 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. It also intends to retain a large consulting firm to supplement its small data science team and 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.
What is the most important step for the healthcare network to take when mapping its existing data to the clinical research partner data?
Answer: D
Explanation:
Identifying fits and gaps in the combined data is crucial to understand whether the datasets are compatible and sufficient for training the AI model effectively.
NEW QUESTION # 21
What is the most important factor when deciding whether or not to select a proprietary AI model?
Answer: B
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 2024:
"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 # 22
CASE STUDY
Please use the following to answer the next question:
A local police department in the United States procured an AI 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 AI system works by surveying 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 to 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 AI 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 AI 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 AI system gives a score of at least 90% and proceed directly with an arrest.
Which AI risk would NOT have been identified during the procurement process based on the categories of information requested by the third-party consultant?
Answer: A
Explanation:
The consultant evaluated accuracy, training data diversity (related to discrimination), and system workings (explainability), but security risks were not assessed during procurement.
NEW QUESTION # 23
CASE STUDY
Please use the following to answer the next question:
You have recently assumed the role of AI Governance leader for a California-based medical technology company. The organization primarily serves hospitals and has recently expanded to include walk-in clinics located within local pharmacies.
The company's core business focuses on diagnostic assistance powered by a large language model LLM and back-office process optimization using Agentic AI, including chatbots, medical record request handling, scheduling and billing.
In preparation for its next round of funding, the board has asked you to prepare an AI Risk report to demonstrate to investors how the company is addressing AI-related risks. In preparing the report you learn that last year the company generated 30 million dollars in gross revenue across the US, EU, India, and South Korea and that vendors are engaged for various activities, including model testing and providing third-party AI solutions for chatbots.
Which of the following best exemplifies human oversight capabilities you should enable under the relevant AI laws?
Answer: A
Explanation:
The correct answer is A because it directly demonstrates meaningful human oversight over AI-generated outcomes, which is a key requirement in AI governance frameworks and regulations such as the EU AI Act. Human oversight requires that a qualified human can review, intervene, and override AI decisions before they produce legal or significant real-world effects. In high-risk contexts like healthcare diagnostics, governance frameworks emphasize "human-in-the-loop" controls to prevent harm and ensure accountability. Option A ensures a licensed medical professional validates the AI output before it is finalized, aligning with safety, accountability, and risk mitigation principles. Other options describe training, system design, or monitoring, which are important governance measures but do not constitute direct oversight of individual AI decisions at the point of impact, making them insufficient under strict regulatory expectations.
NEW QUESTION # 24
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?
Answer: A
Explanation:
Supervised learning is a subcategory of AI and machine learning where labeled datasets are used to train algorithms. This process involves feeding the algorithm a dataset where the input-output pairs are known, allowing the algorithm to learn and make predictions or decisions based on new, unseen data. Reference: AIGP BODY OF KNOWLEDGE, which describes supervised learning as a model trained on labeled data (e.g., text recognition, detecting spam in emails).
NEW QUESTION # 25
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