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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 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 (Q99-Q104):

NEW QUESTION # 99
Which of the following may be permissible uses of an AI system under the EU AI Act EXCEPT?

A. Using biometrics in abduction cases
B. Detecting emotions in a workplace for employee morale
C. Improving the response time for emergency services
D. Detecting emotions in a telemedicine session

Answer: B

Explanation:
The correct answer is D. Emotion recognition in the workplace is flagged as unacceptable or highly restricted under the EU AI Act due to its intrusive nature and potential for misuse.
From the AIGP ILT Guide - EU AI Act Training Module:
"AI systems that monitor individuals' emotions in the workplace or educational settings are listed among prohibited or strictly limited practices under Article 5." AI Governance in Practice Report 2024 supports this interpretation:
"Emotion recognition systems, especially in sensitive contexts such as employment or education, raise significant concerns under EU fundamental rights law and are likely to be restricted." Other uses listed-such as emergency response or emotion detection in healthcare-may fall under lawful and beneficial uses, especially when justified by public interest.

NEW QUESTION # 100
What is the primary objective of continuous monitoring in the lifecycle of an AI tool?

A. To ensure that the AI tool's algorithm evolves independently to adapt to new data without human intervention.
B. To periodically upgrade the AI tool's computational resources to maintain processing efficiency.
C. To restrict the AI tool from accessing external data sources to maintain data privacy.
D. To track and adjust the tool's performance in order to ensure that it meets its goals.

Answer: D

Explanation:
Continuous monitoring aims to track and adjust the AI tool's performance over time to ensure it consistently meets intended goals and operates effectively.

NEW QUESTION # 101
An app developer is creating a to-do app that compiles users ' to-do lists, conducts basic online research about the tasks, prioritizes tasks, and allows users to add descriptions and notes. However, during the app ' s download process, it requests the user ' s name, address, phone number, political and union affiliations.
Which privacy principle relevant to the responsible development of AI systems is most likely violated by this data collection practice?

A. Transparency and explainability.
B. Data portability and subject access rights.
C. Purpose limitation and explicit consent.
D. Data minimization and privacy by design.

Answer: D

Explanation:
Data minimization requires organizations to collect and process only personal information that is adequate, relevant, and necessary for the specified purpose. A basic to-do application may reasonably require limited account or contact information, but political opinions and trade-union affiliations have no apparent necessity for compiling, researching, or prioritizing tasks. Those attributes are also treated as particularly sensitive under European data-protection law. Privacy by design requires privacy safeguards and data-protection principles to be incorporated into the product from the design stage and reflected in default settings.
Therefore, unnecessary collection at installation directly implicates both minimization and privacy by design.
Purpose limitation may also become relevant, but explicit consent does not by itself cure unjustified overcollection. Transparency, portability, and access rights address different privacy obligations and do not best characterize the defect described here.

NEW QUESTION # 102
All of the following may be copyright risks from teachers using generative AI to create course content EXCEPT?

A. Generative AI often creates content without attribution.
B. Content created by an LLM may be protectable under U.S. intellectual property law.
C. Generative AI is generally trained using intellectual property owned by third parties.
D. Students must expressly consent to this use of generative AI.

Answer: D

Explanation:
The correct answer is C. This option concerns consent, which is generally a privacy, transparency, or institutional governance issue rather than a copyright issue. Copyright risk in AI governance usually relates to whether protected third party works were used in model training, whether generated outputs may reproduce copyrighted expression, and whether content is created without proper attribution or clear ownership boundaries. That makes options A, B, and D directly relevant to copyright and intellectual property concerns.
In contrast, whether students must expressly consent deals with notice, fairness, and lawful or ethical use in an educational environment. For AIGP exam purposes, it is important to separate intellectual property risks from privacy and data protection obligations, even when both appear in the same classroom or organizational AI use case.

NEW QUESTION # 103
CASE STUDY
Please use the following to answer the next question:
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address AI governance.
The marketing company has:
- Entered into a contract with the technology company with suitable
representations and warranties.
- Completed an impact assessment on the LLM for this intended use.
- Built technical guidance on how to measure and mitigate bias in the
LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Followed applicable regulatory requirements.
- Created specific legal statements and disclosures regarding the use
of the AI on its client's advertising.
The technology company has:
- Provided guidance and resources to developers to address
environmental concerns.
- Build technical guidance on how to measure and mitigate bias in the
LLM.
- Provided tools and resources to measure bias specific to the LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Mapped and mitigated potential societal harms and large-scale
impacts.
- Followed applicable regulatory requirements and industry standards.
- Created specific legal statements and disclosures regarding the LLM,
including with respect to IP and rights to data.
Which stakeholder is responsible for lawful collection of data for the training of the foundational AI model?

A. The data aggregator.
B. The marketing agency's client.
C. The tech company.
D. The marketing agency.

Answer: C

NEW QUESTION # 104
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