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IAPP AIGP Exam Syllabus Topics:

Topic	Details
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 (Q195-Q200):

NEW QUESTION # 195
CASE STUDY
Please use the following answer the next question:
Good Values Corporation (GVC) is a U.S. educational services provider that employs teachers to create and deliver enrichment courses for high school students. GVC has learned that many of its teacher employees are using generative Al to create the enrichment courses, and that many of the students are using generative Al to complete their assignments.
In particular, GVC has learned that the teachers they employ used open source large language models ("LLM") to develop an online tool that customizes study questions for individual students. GVC has also discovered that an art teacher has expressly incorporated the use of generative Al into the curriculum to enable students to use prompts to create digital art.
GVC has started to investigate these practices and develop a process to monitor any use of generative Al, including by teachers and students, going forward.
What is the best reason for GVC to offer students the choice to utilize generative Al in limited, defined circumstances?

A. To enable students to learn about practical applications of Al.
B. To enable students to learn how to manage their time.
C. To enable students to learn about performing research.
D. To enable students to learn how to use Al as a supportive educational tool.

Answer: D

Explanation:
The best reason for GVC to offer students the choice to utilize generative AI in limited, defined circumstances is to enable students to learn how to use AI as a supportive educational tool. By integrating AI in a controlled manner, students can learn the practical applications of AI and develop skills to use AI responsibly and effectively in their educational pursuits.

NEW QUESTION # 196
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?

A. Establishing a regular schedule for human evaluation of the model's performance, including qualitative assessments.
B. Setting up automated tools to regularly track the model's accuracy, precision and recall rates in real- time.
C. Implementing a formal incident response plan to address incidents that may occur during system operation.
D. Publicly disclosing data with forecasts of secondary and downstream harms to stakeholders.

Answer: D

Explanation:
While transparency is encouraged,publicly disclosing forecasts of secondary harmsisnot a required or standard practicefor internal performance evaluation. Risk assessments and reporting typically remaininternal or shared with regulators.
From theAI Governance in Practice Report2025:
"Organizations must assess secondary risks... but disclosure is subject to context, regulatory requirements, and risk management discretion." (p. 30)

NEW QUESTION # 197
Each of the following actors are typically engaged in the Al development life cycle EXCEPT?

A. Socio-cultural and technical experts.
B. Data architects.
C. Government regulators.
D. Legal and privacy governance experts.

Answer: C

Explanation:
Typically, actors involved in the AI development life cycle include data architects (who design the data frameworks), socio-cultural and technical experts (who ensure the AI system is socio-culturally aware and technically sound), and legal and privacy governance experts (who handle the legal and privacy aspects).
Government regulators, while important, are not directly engaged in the development process but rather oversee and regulate the industry. Reference: AIGP BODY OF KNOWLEDGE and AI development frameworks.

NEW QUESTION # 198
All of the following issues are unique for proprietary AI model deployments EXCEPT?

A. The potential for bias.
B. The necessity of performing conformity assessments.
C. The cost of AI chips.
D. The acquisition of training data.

Answer: A

Explanation:
Biasis a common risk acrossboth proprietary and open-source models, andnot uniqueto proprietary deployments. All AI systems - regardless of origin - require evaluation for fairness, accuracy, and representativeness.
From theAI Governance in Practice Report 2024:
"Bias, discrimination and fairness challenges are present in both open and closed models, regardless of how the model is sourced." (p. 41)

NEW QUESTION # 199
A company initially intended to use a large data set containing personal information to train an AI model. After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model. This is an example of applying which privacy-enhancing technique (PET)?

A. Anonymization.
B. Federated learning.
C. Differential privacy.
D. Pseudonymization.

Answer: A

Explanation:
Anonymization permanently removes personal identifiers from data, preventing re-identification and enhancing privacy.

NEW QUESTION # 200
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