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IAPP AIGP Exam Syllabus Topics:

TopicDetails
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 (Q61-Q66):

NEW QUESTION # 61
A company developed AI technology that can analyze text, video, images and sound to tag content, including the names of animals, humans and objects. What type of AI is this technology classified as?

Answer: B

Explanation:
This AI processes multiple types of data (text, video, images, sound) simultaneously, which classifies it as a multi-modal model.


NEW QUESTION # 62
What is the primary purpose of an AI impact assessment?

Answer: A

Explanation:
The correct answer is D. AI Impact Assessments are primarily used to identify and manage risks and harms associated with AI systems.
From the AIGP Body of Knowledge:
"The goal of an AI impact assessment is to ensure that risks are identified, evaluated, and mitigated prior to or during development and deployment." As further confirmed in the AI Governance in Practice Report 2024 (Part III):
"Risk-based tools like DPIAs and Algorithmic Impact Assessments help identify potential risks to individuals and society, enabling organizations to implement mitigation plans and safeguards." While benefits may be noted in such assessments, the core objective is to manage risks and promote responsible AI.


NEW QUESTION # 63
A company developing and deploying its own AI model would perform all of the following steps to monitor and evaluate the model's performance EXCEPT?

Answer: A

Explanation:
While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or standard practicefor internal performance evaluation. Risk assessments and reporting typically remain internal or shared with regulators.
From theAI Governance in Practice Report 2024:
"Organizations must assess secondary risks... but disclosure is subject to context, regulatory requirements, and risk management discretion." (p. 30)


NEW QUESTION # 64
Scenario:
An organization is evaluating different AI models for integration into its internal workflows. Before moving forward with a particular AI solution from a third-party vendor, the governance team needs to assess the ethical and operational implications of the model.
The most important policy to assess the operations of an AI model is to follow the:

Answer: C

Explanation:
The correct answer isA. TheAcceptable Use Policy (AUP)sets the primary terms and conditions under which the AI model can be used, including limitations, prohibited uses, and operational constraints.
From the AIGP ILT Guide:
"The AUP is the most critical document to assess what you can and cannot do with the AI system. It governs use cases, outlines forbidden uses (e.g., disinformation), and supports responsible AI practices." Also confirmed in the AI Governance in Practice Report2025(Third-Party AI Assurance section):
"Reviewing and adhering to the acceptable use policy ensures that the model is being used in a manner that aligns with both provider expectations and ethical AI practices."


NEW QUESTION # 65
Each of the following actors are typically engaged in the Al development life cycle EXCEPT?

Answer: A


NEW QUESTION # 66
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