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100% Pass Marvelous AIGP - Latest IAPP Certified Artificial Intelligence Governance Professional Test Fee

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IAPP AIGP Exam Syllabus Topics:

Topic
Details

Topic 1

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 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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q52-Q57):

NEW QUESTION # 52
What is the primary purpose of an Al impact assessment?

A. To define and evaluate the legal risks associated with developing an Al system.
B. To identify and measure the benefits of an Al system.
C. To define and document the roles and responsibilities of Al stakeholders.
D. Anticipate and manage the potential risks and harms of an Al system.

Answer: D

Explanation:
The primary purpose of an AI impact assessment is to anticipate and manage the potential risks and harms of an AI system. This includes identifying the possible negative outcomes and implementing measures to mitigate these risks. This process helps ensure that AI systems are developed and deployed in a manner that is ethically and socially responsible, addressing concerns such as bias, fairness, transparency, and accountability. The assessment often involves a thorough evaluation of the AI system's design, data inputs, outputs, and the potential impact on various stakeholders. This approach is crucial for maintaining public trust and adherence to regulatory requirements.

NEW QUESTION # 53
Scenario:
A company is using different types of AI systems to enhance consumer engagement. These include chatbots, recommendation engines, and automated content generation tools.
Which of the following situations would be least likely to raise concerns under existing consumer protection laws?

A. An AI customer service system claiming that it is as accurate as a human support agent
B. An AI tool using scraped digital content to generate news summaries on a publishing website
C. An AI algorithm being used in a credit decision-making process by a financial institution
D. An online platform offering recommendations to its users by displaying user-specific content and targeted advertisements

Answer: D

Explanation:
The correct answer is D. Personalized content and advertisements, as long as properly disclosed and non- deceptive, are not generally a consumer protection issue under current legal regimes.
From the AI Governance in Practice Report 2024 (Consumer Protection Section):
"Standard practices like targeted advertising and recommendations are widely accepted provided they comply with transparency and consent requirements." Meanwhile, credit decision-making and misleading AI performance claims (Answers A and B) have already led to regulatory enforcement.
The AIGP ILT Guide highlights:
"Deceptive claims, biased financial decisions, and unauthorized data use may violate consumer protection and privacy laws. Advertising personalization is routine but must be disclosed appropriately."

NEW QUESTION # 54
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?

A. Accountable and Transparent.
B. Tested and Effective.
C. Secure and Resilient.
D. Explainable and Interpretable.

Answer: B

Explanation:
The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework.
The focus is more on the system's ability to function safely, securely, and transparently in a way that stakeholders can understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.

NEW QUESTION # 55
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 enable and encourage participation by external stakeholders.
C. To establish diverse, equitable and inclusive processes.
D. To determine responsibility for allocating budgetary resources.

Answer: A

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 # 56
Which type of existing assessment could best be leveraged to create an Al impact assessment?

A. A safety impact assessment.
B. An environmental impact assessment.
C. A security impact assessment.
D. A privacy impact assessment.

Answer: D

Explanation:
A privacy impact assessment (PIA) can be effectively leveraged to create an AI impact assessment. A PIA evaluates the potential privacy risks associated with the use of personal data and helps in implementing measures to mitigate those risks. Since AI systems often involve processing large amounts of personal data, the principles and methodologies of a PIA are highly applicable and can be extended to assess broader impacts, including ethical, social, and legal implications of AI. Reference: AIGP Body of Knowledge on Impact Assessments.

NEW QUESTION # 57
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