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IAPP AIGP Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Governance Implementation- Organizational Governance
  • 1. Governance Structures
  • 2. Policies and Procedures
  • 3. Roles and Responsibilities (RACI)
- Data Governance for AI
  • 1. Data Privacy and Protection
  • 2. Data Quality and Integrity
Topic 2: 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
Topic 3: Foundations of AI Governance- Impacts of AI
  • 1. Risks of AI
  • 2. Benefits 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
Topic 4: AI Laws, Regulations, and Standards- Global AI Frameworks
  • 1. ISO/IEC Standards
  • 2. UNESCO AI Ethics
  • 3. OECD AI Principles
- Regional and National Regulations
  • 1. US State and Federal Regulations
  • 2. National AI Strategies
  • 3. EU AI Act
Topic 5: AI Governance Management- Emerging Issues
  • 1. Generative AI
  • 2. Future Trends
  • 3. Ethical AI
- Life Cycle Management
  • 1. Auditing and Monitoring
  • 2. Third-party management
  • 3. Incident Management

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Questions and Answers for the AIGP Exam, Authentic 2026

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q45-Q50):

NEW QUESTION # 45
An artist has been using an AI tool to create digital art and would like to ensure that it has copyright protection in the United States. Which of the following is most likely to enable the artist to receive copyright protection?

Answer: A

Explanation:
Copyright protection typically requires a human author's creative input, so modifying AI-generated images in a creative way demonstrates the artist's originality needed for copyright.


NEW QUESTION # 46
The planning phase of the Al life cycle articulates all of the following EXCEPT the?

Answer: D

Explanation:
The planning phase of the AI life cycle typically includes defining the objective of the model, choosing the appropriate architecture, and understanding the context in which the model will operate. However, the approach to governance is usually established as part of the overall AI governance framework, not specifically within the planning phase. Governance encompasses broader organizational policies and procedures that ensure AI development and deployment align with legal, ethical, and operational standards. Reference: AIGP Body of Knowledge, AI lifecycle planning phase section.


NEW QUESTION # 47
CASE STUDY
Please use the following to answer the next question:
A small local flower delivery company operates a few stores and has a limited IT budget. To reduce the time spent on customer service, which is a major drain on employee time, the company plans to implement a simple but high-performance generative AI chatbot on its website. The chatbot will provide real-time, fact- based responses to customer inquiries and assist with order processing, helping to reduce incoming phone calls.
The company currently uses a single on-premises server for its order database and email system and has not yet partnered with a cloud provider.
An internal data privacy policy governs the use of customer data. This policy, written before the adoption of AI, allows the use or transfer of customer data beyond name and contact information but requires that all such data be deleted after the order is completed. The company prefers to maintain this policy without changes.
What deployment option is the simplest, quickest, and most cost-effective option across the various potential AI models for this particular company?

Answer: A

Explanation:
Software-as-a-Service accessed through an API is the most practical deployment approach for a small organization with limited IT infrastructure and budget. SaaS allows the company to consume a provider- managed application without purchasing and maintaining the substantial computing infrastructure normally needed for modern generative AI. Under NIST ' s cloud-computing model, SaaS consumers use provider applications while the provider manages the underlying network, servers, operating systems, storage, and much of the supporting infrastructure. This makes implementation comparatively rapid and scalable. Running the model on the existing on-premises server could require hardware upgrades and specialized maintenance.
A private-cloud deployment introduces additional infrastructure and administrative complexity. Edge deployment on customers ' devices would also create substantial technical and compatibility challenges.
Therefore, considering the company ' s resources and desired speed of deployment, C is the strongest option.


NEW QUESTION # 48
Each of the following actors are typically engaged in the AI development life cycle EXCEPT:

Answer: B

Explanation:
Government regulators oversee compliance but are not typically involved directly in the AI development life cycle.


NEW QUESTION # 49
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Training an AI model is time-consuming because of?

Answer: A,C,E

Explanation:
Training an AI model is time-consuming primarily due tomodel complexity,large data volumes, and the need forhigh-quality, well-prepared data.
From theAI Governance in Practice Report 2025:
"Most AI requires sizeable amounts of high-quality data... to ensure desired and accurate output." (p. 15)
"The accuracy of AI model outputs depends significantly on the quality of their inputs." (p. 24)
"Complex AI systems... with many parameters... result in long development and training phases." (p. 32) B . Maturity of governanceaffects oversight, not training time.
D . Number of stakeholdersaffects alignment, not direct training duration.


NEW QUESTION # 50
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