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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 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.

Topic 3

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 4

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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q121-Q126):

NEW QUESTION # 121
The most important factor in ensuring fairness when training an AI system is:

A. The data attributes and variability.
B. The architecture and model selection.
C. The data labeling and classification.
D. The model accuracy and scale.

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 # 122
If it is possible to provide a rationale for a specific output of an AI system, that system can best be described as:

A. Transparent.
B. Explainable.
C. Reliable.
D. Accountable.

Answer: B

Explanation:
An AI system that can provide a rationale for its specific outputs is considered explainable because it offers understandable reasons for its decisions.

NEW QUESTION # 123
What is the main purpose of accountability structures under the Govern function of the NIST Al Risk Management Framework?

A. To determine responsibility for allocating budgetary resources.
B. To establish diverse, equitable and inclusive processes.
C. To enable and encourage participation by external stakeholders.
D. To empower and train appropriate cross-functional teams.

Answer: D

Explanation:
The NIST AI Risk Management Framework's Govern function emphasizes the importance of establishing accountability structures that empower and train cross-functional teams. This is crucial because cross- functional teams bring diverse perspectives and expertise, which are essential for effective AI governance and risk management. Training these teams ensures that they are well-equipped to handle their responsibilities and can make informed decisions that align with the organization's AI principles and ethical standards. Reference:
NIST AI Risk Management Framework documentation, Govern function section.

NEW QUESTION # 124
What is the most important reason for documenting risks when developing an AI system?

A. To provide transparency to stakeholders.
B. To promote knowledge sharing.
C. To align with industry standards.
D. To mitigate potential liability.

Answer: D

Explanation:
The most critical reason for documenting AI-related risks is toreduce exposure to legal, regulatory, and reputational liabilities. Clear documentation demonstrates thatrisks were identified, assessed, and addressed, which is essential for accountability and defensibility in the face of audits, litigation, or enforcement actions.
From theAI Governance in Practice Report 2024:
"An effective AI governance model is about collective responsibility... which should encompass oversight mechanisms such as privacy, accountability, compliance." (p. 13)
"Accountability... is based on the idea that there should be a person or entity that is ultimately responsible for any harm resulting from the use of the data, algorithm and AI system's underlying processes." (p. 28) While transparency, alignment with standards, and knowledge sharing are all secondary benefits,risk documentation's primary role is liability mitigation.

NEW QUESTION # 125
When should an ethical impact assessment for AI be performed?

A. After the AI system has encountered ethical issues in real-world applications.
B. At the initial stages of AI system development and continually throughout its lifecycle.
C. Then the number of AI system users has reached a predefined threshold.
D. When the system is ready for production and deployment.

Answer: B

Explanation:
Ethical impact assessments should begin at the initial development stages and continue throughout the AI system's lifecycle to proactively identify and address ethical risks.

NEW QUESTION # 126
......

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