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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 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 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 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 (Q146-Q151):

NEW QUESTION # 146
CASE STUDY
Please use the following to answer the next question:
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.
All of the following are obligations of the company as a data controller when implementing its AI system EXCEPT?

A. Allowing data subject access requests (DSARs).
B. Conducting a Data Protection Impact Assessment (DPIA) / Privacy Impact Assessment (PIA).
C. Ensuring that third-party processors are based in the same country as the company.
D. Implementing technical and organizational measures.

Answer: C

Explanation:
While the company must ensure data protection compliance, it is not required that third-party processors be located in the same country; instead, adequate data protection measures and legal safeguards must be in place.

NEW QUESTION # 147
What is the best method to apply to an AI model so that it is statistically unlikely that a specific piece of training data can be identified from a model's output?

A. Differential privacy.
B. Homomorphic encryption.
C. Data compartmentalization.
D. Data sharding.

Answer: A

Explanation:
Differential privacy adds controlled noise to the training process, making it statistically unlikely to identify specific training data from the model's outputs.

NEW QUESTION # 148
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent.
The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
Which other stakeholder groups should be involved in the selection and implementation of the Al hiring tool?

A. Finance and Legal.
B. Supply Chain and Marketing.
C. Litigation and Product Development.
D. Marketing and Compliance.

Answer: A

Explanation:
In the selection and implementation of the AI hiring tool, involving Finance and Legal is crucial. The Finance team is essential for assessing cost implications, budget considerations, and financial risks. The Legal team is necessary to ensure compliance with applicable laws and regulations, including those related to data privacy, employment, and anti-discrimination. Involving these stakeholders ensures a comprehensive evaluation of both the financial viability and legal compliance of the AI tool, mitigating potential risks and aligning with organizational objectives and regulatory requirements.

NEW QUESTION # 149
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?

A. The complexity of the AI model.
B. The quality of the training data.
C. The maturity of AI governance.
D. The number of stakeholders.
E. The volume of training data.

Answer: A,B,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 2024:
"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 # 150
Scenario:
A large multinational organization is rolling out a company-wide AI governance initiative. To build awareness and support adoption, they are evaluating different ways to train employees and stakeholders across departments, including legal, technical, marketing, and customer-facing roles.
Which of the following typical approaches is a largeorganization leastlikely touse to responsibly train stakeholders on AI terminology, strategy and governance?

A. Providing role-specific training, based on whether the organization uses a centralized, federated or decentralized governance model
B. Providing information and education to customers and users to understand the capabilities and limitations of the AI tools with which they interact
C. Providing training on AI ethics, based on the extent to which the organization seeks to promote a responsible AI culture
D. Providing all technical employees education on AI development so they can retool and participate in the development of AI systems

Answer: D

Explanation:
The correct answer isA. While educating technical staff is important, expectingall technical employees to be retooled as AI developersis unrealistic and not aligned with scalable governance practices.
From the AIGP ILT Guide:
"Training approaches should berole-specificand align with the individual's function and responsibilities...
Organizations typically do not expect every technical role to participate in model development." The AI Governance in Practice Report2025supports tailored approaches:
"Cross-functional training should be specific to the individual's role and exposure to AI risk... Role-based education supports scalability and comprehension." Thus,broad development training for all technical employeesis the least practical and least likely approach.

NEW QUESTION # 151
......

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