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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 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 3
  • 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 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 (Q118-Q123):

NEW QUESTION # 118
Why is it important that conformity requirements are satisfied before an AI system is released into production?

Answer: C

Explanation:
Conformity assessmentsare a core requirement under theEU AI Actfor high-risk systems and serve to confirm that the AI meetsregulatory, safety, and ethical standardsbefore it is put into production.
From theAI Governance in Practice Report 2024:
"Conformity assessments... ensure that systems comply with legal requirements, safety criteria, and intended purpose before being placed on the market." (p. 34)
"They are a critical step to demonstrate safety and trustworthiness in AI deployment." (p. 35)


NEW QUESTION # 119
Scenario:
A global organization wants to align with international frameworks on AI governance. They are reviewing guidance from the OECD on how to incorporate broader governance tools into their AI program.
Codes of conductandcollective agreementsare what type of assessment tools as defined by theOrganization for Economic Cooperation and Development (OECD)?

Answer: C

Explanation:
The correct answer isB - Procedural. TheOECD Framework for Classifying AI Systemscategorizescodes of conduct and collective agreementsasprocedural toolsbecause they guide internal governance and decision- making processes.
From the AIGP ILT Participant Guide - Global Governance Models:
"Procedural tools include internal codes of conduct, collective agreements, and procedural audits that guide governance without necessarily involving technical measurement." AI Governance in Practice Report2025elaborates:
"These procedural tools support internal accountability mechanisms and ethics compliance frameworks...
they are part of soft governance."
These toolsdo not measure or analyze technical performance, hence they arenot technical or analytic.


NEW QUESTION # 120
Retrieval-Augmented Generation (RAG) is defined as?

Answer: D

Explanation:
Retrieval-Augmented Generation (RAG)enhances Large Language Models (LLMs) by integratingexternal, up-to-date, or proprietary informationinto the generation pipeline-allowing the model tofetch relevant factsfrom a trusted knowledge source at query time.
Though RAG is not defined directly in the IAPP documents, it is a widely recognized technique in AI governance for ensuringmore accurate and contextually grounded outputs, especially inregulated or high- stakes environmentswhere hallucinations are a concern.
* B, C, and Ddescribe optimization or bias mitigation-not the core function of RAG.


NEW QUESTION # 121
CASE STUDY
A company is considering the procurement of an AI system designed to enhance the security of IT infrastructure. The AI system analyzes how users type on their laptops, including typing speed, rhythm and pressure, to create a unique user profile. This data is then used to authenticate users and ensure that only authorized personnel can access sensitive resources.
The data processed by the AI system would be classified as:

Answer: B

Explanation:
The correct answer isD.Keystroke dynamics, used to identify individuals, fallunder biometricdata, which isa specialcategory of personaldata underthe GDPR and other frameworks.
From the AI Governance in Practice Report2025:
"Keystroke dynamics may constitute biometric data if used to uniquely identify an individual... Biometric data is classified as special category personal data and requires higher protection standards." Also reflected in ILT Participant Guide:
"Biometric data, such as facial images, voiceprints, iris scans or keystroke patterns, are treated as special category data when they are used for the purpose of uniquely identifying individuals."


NEW QUESTION # 122
Which of the following most encourages accountability over AI systems?

Answer: D

Explanation:
Defining clear roles and responsibilities for AI stakeholders is essential to establish accountability throughout the AI system's lifecycle.


NEW QUESTION # 123
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