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IAPP AIGP Exam Syllabus Topics:

Section	Objectives
Foundations of AI Governance	- Impacts of AI 1. Benefits of AI 2. Risks of AI - AI Governance Fundamentals 1. Key AI Terminology and Concepts 2. Defining AI and its different approaches 3. Understanding the AI Development Life Cycle
AI Governance Implementation	- Data Governance for AI 1. Data Privacy and Protection 2. Data Quality and Integrity - Organizational Governance 1. Governance Structures 2. Policies and Procedures 3. Roles and Responsibilities (RACI)
AI Governance Management	- Emerging Issues 1. Ethical AI 2. Generative AI 3. Future Trends - Life Cycle Management 1. Auditing and Monitoring 2. Third-party management 3. Incident Management
AI Risk Management	- Risk Frameworks and Standards 1. NIST AI Risk Management Framework (RMF) 2. ISO 31000 Risk Management - Operationalizing Risk Management 1. Risk identification and assessment 2. Risk mitigation strategies
AI Laws, Regulations, and Standards	- Regional and National Regulations 1. National AI Strategies 2. US State and Federal Regulations 3. EU AI Act - Global AI Frameworks 1. UNESCO AI Ethics 2. ISO/IEC Standards 3. OECD AI Principles

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AIGP Practice Materials: IAPP Certified Artificial Intelligence Governance Professional & AIGP Real Exam Dumps - DumpsValid

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q141-Q146):

NEW QUESTION # 141
You are a privacy program manager at a large e-commerce company that uses an AI tool to deliver personalized product recommendations based on visitors' personal information that has been collected from the company website, the chatbot and public data the company has scraped from social media.
A user submits a data access request under an applicable US state privacy law, specifically seeking a copy of their personal data, including information used to create their profile for product recommendations.
What is the most challenging aspect of managing this request?

A. The data subject's data is structured data that can be searched, compiled and reviewed only by an automated tool.
B. Some of the visitor's data is synthetic data that the company does not have to provide to the data subject.
C. The data subject is not entitled to receive a copy of their data because some of it was scraped from public sources.
D. Some of the data subject's data is unstructured data and you cannot untangle it from the other data, including information about other individuals.

Answer: D

Explanation:
Unstructured data that cannot be easily separated from information about other individuals makes fulfilling the data access request complex while ensuring privacy for others.

NEW QUESTION # 142
When should an ethical impact assessment for AI be performed?

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

Answer: C

Explanation:
Ethical impact assessment should begin early and remain iterative throughout the AI lifecycle, making D correct. Waiting until production, a user threshold, or an actual ethical incident would make the assessment reactive rather than preventive. UNESCO describes ethical impact assessment as an " ethics by design " mechanism covering research, design, development, deployment, use, maintenance, monitoring, and eventual termination, while emphasizing its particular value during early research and design. OECD-listed assessment guidance similarly states that the first assessment should occur during design, with reassessments at development, deployment, and other significant lifecycle stages. NIST ' s AI RMF Playbook also recommends impact assessment at key stages because risks, operating conditions, stakeholders, data, and system capabilities can change over time. Continuous reassessment therefore supports proactive identification and mitigation of emerging harms.

NEW QUESTION # 143
Scenario:
An organization is developing a powerful general-purpose AI (GPAI) model that has systemic impact. The compliance team is assessing what legal obligations apply under the EU AI Act.
Under the EU AI Act, which of the following compliance actions applies only to General Purpose AI models with systemic risk?

A. Publishing a detailed summary of the data used to train the model
B. Maintaining up-to-date technical documentation, including testing details
C. Making information available to downstream providers who integrate the model into their AI systems
D. Implementing an intellectual property policy to comply with EU copyright laws

Answer: A

Explanation:
The correct answer is A. Only GPAI models with systemic risk must publish a detailed summary of training data to meet transparency and accountability standards under the EU AI Act.
From the AI Governance in Practice Report 2024 (EU AI Act Section):
"For GPAI systems with systemic risk, providers must publish sufficiently detailed summaries of the content used to train the model." Also, the AIGP ILT Guide confirms:
"The obligation to disclose summaries of training data applies only to systemic-risk GPAI models, not all general-purpose models or high-risk systems." This unique requirement is part of the Act's effort to increase transparency and auditability for powerful foundational models.

NEW QUESTION # 144
What is the most significant risk of deploying an AI model that can create realistic images and videos?

A. Copyright infringement.
B. Downstream harms.
C. Security breaches.
D. Output cannot be protected.

Answer: B

Explanation:
The greatest risk from AI systems generatingrealistic synthetic mediaisdownstream harm, such as deepfakes, misinformation, reputational damage, and erosion of trust.
From theAI Governance in Practice Report 2024:
"With generative AI, downstream harms such as deception, reputational damage, misinformation, and manipulation can emerge even if original use was lawful." (p. 55-56)

NEW QUESTION # 145
During the first month when the company monitors the model for bias, it is most important to?

A. Provide regular awareness training.
B. Document the results of final decisions made by the human underwriter.
C. Continue disparity testing.
D. Analyze the quality of the training and testing data.

Answer: C

Explanation:
Theinitial deployment phaseof an AI model is critical forpost-deployment monitoring. When tracking forbias, the most important task is tocontinue disparity testingto determine whether outputs differ across protected groups.
From theAI Governance in Practice Report 2025:
"Performance monitoring protocols... should include mechanisms to assess and measure disparities in outcomes across different demographic groups." (p. 12)
"Bias may not be evident during pre-deployment testing but can emerge in real-world use." (p. 41) B . Awareness trainingis helpful, but not a technical bias mitigation activity.
C . Analyzing training datais apre-deploymenttask.
D . Documenting human decisionsmay support auditability but doesn't detect bias in AI outputs.

NEW QUESTION # 146
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