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IAPP AIGP Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 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.
Thema 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.
Thema 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.

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AIGP Studienmaterialien: IAPP Certified Artificial Intelligence Governance Professional & AIGP Zertifizierungstraining

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IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q45-Q50):

45. Frage
Which of the following deployments of generative Al best respects intellectual property rights?

A. The system produces content that includes trademarks and copyrights.
B. The system provides attribution to creators of publicly available information.
C. The system categorizes and applies filters to content based on licensing terms.
D. The system produces content that is modified to closely resemble copyrightedwork.

Antwort: C

Begründung:
Respecting intellectual property rights means adhering to licensing terms and ensuring that generated content complies with these terms. A system that categorizes and applies filters based on licensing terms ensures that content is used legally and ethically, respecting the rights of content creators. While providing attribution is important, categorization and application of filters based on licensing terms are more directly tied to compliance with intellectual property laws. This principle is elaborated in the IAPP AIGP Body of Knowledge sections on intellectual property and compliance.

46. Frage
What is the main purpose of accountability structures under the Govern function of the NIST Al Risk Management Framework?

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

Antwort: A

Begründung:
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.

47. Frage
A company ' s AI-powered hiring tool is found to be consistently ranking male candidates higher than female candidates with similar qualifications.
Which of the following is the most immediate and critical governance action required to address this issue?

A. Log the incident in the company ' s central AI incident management system.
B. Notify cross-functional stakeholders.
C. Conduct a comprehensive audit of the AI system ' s entire lifecycle.
D. Initiate a full-scale retraining of the AI model with a more balanced dataset.

Antwort: A

Begründung:
The correct answer is A because the most immediate governance step when a significant AI issue is identified is to formally log the incident within the organization's incident management system. AI governance frameworks emphasize structured incident response processes to ensure issues are properly documented, tracked, escalated, and addressed in a controlled manner. Logging the incident triggers established workflows, including investigation, stakeholder notification, and remediation planning. While notifying stakeholders, auditing the system, or retraining the model are important follow-up actions, they should occur after the issue is formally recorded and managed through governance channels. This ensures accountability, traceability, and consistent handling of risks, particularly in cases involving bias and potential discrimination.

48. Frage
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model.
Which of the following is NOT a best practice apply during the testing?

A. Testing should minimize human involvement to the extent practicable.
B. The test data should be anonymized to the extent practicable.
C. The test data should be representative of the expected operational data.
D. Testing should be performed specific to the intended uses.

Antwort: A

Begründung:
Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model. Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.

49. Frage
An organization that provides AI services to clients has decided to adopt ISO/IEC 42001:2023.
How does the standard define the relationship between the AI policy and the other policies already in place at the organization?

A. The AI policy should refer to other organizational policies.
B. The AI policy should address gaps identified from a management review of other organizational policies.
C. The AI policy should include AI-specific topics that do not intersect with other organizational policies.
D. The AI policy should absorb relevant components of other organizational policies.

Antwort: A

Begründung:
ISO/IEC 42001:2023 establishes an Artificial Intelligence Management System designed to integrate AI governance into an organization ' s existing management structure rather than create an isolated governance regime. Its AI-policy requirements state that the policy should refer, where relevant, to other organizational policies. Therefore, A most accurately describes the relationship. The standard does not require the organization to absorb all related security, privacy, quality, risk, or compliance policies into one AI policy, making B incorrect. C is inconsistent with integrated governance because AI issues commonly intersect with information security, privacy, procurement, data governance, human resources, and enterprise risk management. Management review is an important management-system activity, but D does not state the relationship required between the AI policy and existing policies. ISO/IEC 42001 consequently favors coordinated and mutually aligned organizational governance.

50. Frage
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