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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 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 4	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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q79-Q84):

NEW QUESTION # 79
What is the main purpose of accountability structures under the Govern function of the NIST AI Risk Management Framework?

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

Answer: A

Explanation:
Accountability structures under the NIST AI Risk Management Framework's Govern function focus on empowering and training cross-functional teams to manage AI risks effectively.

NEW QUESTION # 80
Which of the following use cases would be best served by a non-AI solution?

A. A non-profit wants to develop a social media presence.
B. A customer service agency wants to automate answers to common questions.
C. An e-commerce provider wants to make personalized recommendations.
D. A business analyst wants to develop advertising campaigns.

Answer: A

NEW QUESTION # 81
What is the primary objective of continuous monitoring in the lifecycle of an AI tool?

A. To restrict the AI tool from accessing external data sources to maintain data privacy.
B. To periodically upgrade the AI tool's computational resources to maintain processing efficiency.
C. To ensure that the AI tool's algorithm evolves independently to adapt to new data without human intervention.
D. To track and adjust the tool's performance in order to ensure that it meets its goals.

Answer: D

Explanation:
Continuous monitoring aims to track and adjust the AI tool's performance over time to ensure it consistently meets intended goals and operates effectively.

NEW QUESTION # 82
According to the Singapore Model AI Governance Framework, all of the following are recommended measures to promote the responsible use of AI EXCEPT:

A. Employing human-over-the-loop protocols for high-risk systems.
B. Adapting the existing governance structure to algorithmic decision-making.
C. Determining the level of human involvement in algorithmic decision-making.
D. Establishing communications and collaboration among stakeholders.

Answer: A

Explanation:
According to the Singapore Model AI Governance Framework, employing human-over-the-loop protocols is not a recommended measure to promote the responsible use of AI. Instead, the framework suggests determining the appropriate level of human involvement in AI-augmented decision-making based on a risk assessment. This approach allows organizations to decide whether a human should be in the loop, on the loop, or out of the loop, depending on factors such as the potential harm to individuals and the reversibility of decisions. The goal is to minimize risks while maintaining operational feasibility.

NEW QUESTION # 83
According to the GDPR's transparency principle, when an AI system processes personal data in automated decision-making, controllers are required to provide data subjects specific information on?

A. The contact details of the data protection officer and the data protection national authority.
B. The data protection impact assessments carried out on the AI system and legal bases for processing.
C. The existence of automated decision-making and meaningful information on its logic and consequences.
D. The personal data used during processing, including inferences drawn by the AI system about the data.

Answer: C

Explanation:
GDPR's transparency principle mandates that data subjects be informed about the existence of automated decision-making, along with meaningful information about the logic involved and the potential consequences.

NEW QUESTION # 84
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