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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 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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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q110-Q115):

NEW QUESTION # 110
After initially deploying a third-party AI model, you learn the developer has released a new version.
As deployer of this third-party model, what should you do?

A. Communicate necessary updates to your users.
B. Retrain the model.
C. Audit the model.
D. Seek input from data scientists.

Answer: C

Explanation:
When anew versionof a third-party model is released, the deployer must ensure it still meets safety, performance, and compliance requirements - which calls for aformal audit.
From theAI Governance in Practice Report 2024:
"Any updates or changes to AI systems should trigger a re-evaluation to ensure continued compliance and performance." (p. 12)
"Post-market monitoring includes reassessing the impact of updated models or retraining." (p. 35)

NEW QUESTION # 111
According to the GDPR, an individual has the right to have a human confirm or replace an automated decision unless that automated decision?

A. Is deemed to solely benefit the individual and includes documented legitimate interests.
B. Is authorized by applicable Ell law and includes suitable safeguards.
C. Is necessary for entering into or performing under a contract between the data subject and data controller.
D. Is authorized with the data subject s explicit consent.

Answer: D

Explanation:
According to the GDPR, individuals have the right to not be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significantly affects them. However, there are exceptions to this right, one of which is when the decision is based on the data subject's explicit consent. This means that if an individual explicitly consents to the automated decision-making process, there is no requirement for human intervention to confirm or replace the decision. This exception ensures that individuals can have control over automated decisions that affect them, provided they have given clear and informed consent.

NEW QUESTION # 112
What is the primary purpose of conducting ethical red-teaming on an Al system?

A. To improve the model's accuracy.
B. To simulate model risk scenarios.
C. To ensure compliance with applicable law.
D. To identify security vulnerabilities.

Answer: B

Explanation:
The primary purpose of conducting ethical red-teaming on an AI system is to simulate model risk scenarios.
Ethical red-teaming involves rigorously testing the AI system to identify potential weaknesses, biases, and vulnerabilities by simulating real-world attack or failure scenarios. This helps in proactively addressing issues that could compromise the system's reliability, fairness, and security. Reference: AIGP Body of Knowledge on AI Risk Management and Ethical AI Practices.

NEW QUESTION # 113
What are the roles and responsibilities of deployers of a proprietary model? (Choose three.)

A. Technical performance.
B. System documentation.
C. Ethical design.
D. Ethical testing.
E. Regulatory compliance.

Answer: A,D,E

Explanation:
Deployers of a proprietary model are responsible for ethical testing, ensuring technical performance, and maintaining regulatory compliance when using the model in their environment and for their use case.

NEW QUESTION # 114
Why is it important that conformity requirements are satisfied before an AI system is released into production?

A. To comply with legal and regulatory standards, ensuring the AI system is safe and trustworthy.
B. To ensure the AI system is easy for end-users to operate.
C. To ensure the visual design is fit-for-purpose.
D. To guarantee interoperability of the AI system across multiple platforms and environments.

Answer: A

Explanation:
Conformity assessmentsare a core requirement under theEU AI Actfor high-risk systems and serve to confirm that the AI meetsregulatory, safety, and ethical standardsbefore it is put into production.
From theAI Governance in Practice Report 2024:
"Conformity assessments... ensure that systems comply with legal requirements, safety criteria, and intended purpose before being placed on the market." (p. 34)
"They are a critical step to demonstrate safety and trustworthiness in AI deployment." (p. 35)

NEW QUESTION # 115
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