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IAPP AIGP Exam Syllabus Topics:

Topic
Details

Topic 1

Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.

Topic 2

Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.

Topic 3

Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.

Topic 4

Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q10-Q15):

NEW QUESTION # 10
Which risk management framework/guide/standard focuses on value-based engineering methodology?

A. ISO 31000 Guidelines (Risk Management).
B. IEEE 7000-2021 Standard Model Process for Addressing Ethical Concerns during System Design.
C. Council of Europe Human Rights, Democracy, and the Rule of Law Assurance Framework (HUDERIA) for Al Systems.
D. ISO/IEC Guide 51 (Safety).

Answer: B

Explanation:
The IEEE 7000-2021 Standard focuses on a value-based engineering methodology for addressing ethical concerns during system design. This standard guides engineers and organizations in integrating ethical considerations into the design and development processes of AI systems, ensuring that these technologies are developed responsibly and align with human values. Reference: AIGP Study Material, section on risk management frameworks and standards.

NEW QUESTION # 11
An AI system's function, the industry and the location in which it operates are important factors in considering which of the following?

A. Internal governance needs.
B. Organizational accountability.
C. Explainability of results.
D. Diversity of data sources.

Answer: A

Explanation:
An AI system'sfunction,industry, anddeployment locationdefine itsrisk profile, which directly influences theinternal governance structuresan organization must put in place.
From theAI Governance in Practice Report 2024:
"There are many challenges and potential solutions for AI governance, each with unique proximityand significance based on an organization's role, footprint, broader risk-governance profile and maturity." (p. 4)
"AI governance starts with defining the corporate strategy for AI... and formulating policy standards and operational procedures to reflect industry, use case, and location." (p. 11)
* A- Organizational accountability is broader and not directly scoped by industry or function.
* C- Diversity of data sources is tied to data strategy.
* D- Explainability is more influenced by model type, not use context.

NEW QUESTION # 12
What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running Al applications?

A. GPUs run many tasks concurrently, resulting in faster processing.
B. The number of transistors on GPUs doubles every two years, making thechips smaller and lighter.
C. GPUs can access memory quickly, resulting in lower latency than CPUs.
D. GPUs can run every task on a computer, making them more robust than CPUs.

Answer: A

Explanation:
GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to run many tasks concurrently, which significantly enhances processing speed. This parallel processing capability makes GPUs ideal for handling the large-scale computations required in AI and deep learning tasks. Reference:
AIGP BODY OF KNOWLEDGE, which explains the importance of compute infrastructure in AI applications.

NEW QUESTION # 13
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.
The most significant risk from combining the healthcare network's existing data with the clinical research partner data is?

A. Reputational risk.
B. Privacy risk.
C. Security risk.
D. Operational risk.

Answer: B

Explanation:
The most significant risk from combining the healthcare network's existing data with the clinical research partner data is privacy risk. Combining data sets, especially in healthcare, often involves handling sensitive information that could lead to privacy breaches if not managed properly. De-identified data can still pose re-identification risks when combined with other data sets. Ensuring privacy involves implementing robust data protection measures, maintaining compliance with privacy regulations such as HIPAA, and conducting thorough privacy impact assessments. Reference: AIGP Body of Knowledge on Data Privacy and Security.

NEW QUESTION # 14
A shipping service based in the US is looking to expand its operations into the EU. It utilizes an in-house developed multimodal AI model that analyzes all personal data collected from shipping senders and recipients, and optimizes shipping routes and schedules based on this data.
As they expand into the EU, all of the following descriptions should be included in the technical documentation for their AI model EXCEPT?

A. A description of the prioritization of the risks of deployment of the AI system.
B. A description of the appropriateness of the performance metrics for the specific AI system.
C. A detailed description of the elements of the AI system and of the process for its development.
D. A general description of the AI system.

Answer: A

Explanation:
The EU AI Act outlines what must be included intechnical documentationfor high-risk systems. These requirements are designed to supportconformity assessment, transparency, and traceability.
From theAI Governance in Practice Report 2024:
"It mandates drawing up technical documentation... must include a general description of the AI system, the intended purpose, and a detailed description of the elements and development process." (p. 34)
"Documentation... includes training, testing, evaluation procedures, andappropriateness of performance metrics." (p. 34-35) Therisk management systemis addressed separately through arisk management plan, not within the technical documentation itself.
Thus:
* A, C, and Dare explicitly required in thetechnical documentation.
* B, while important, is part of therisk management process, not a required section oftechnical documentation.

NEW QUESTION # 15
......

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