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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional
Exam Number:	AIGP
Available Languages:	English
Passing Score:	300
Certificate Validity Period:	2 Years
Related Certifications:	CIPT CIPP/A CIPP/US CIPM CIPP/E
Exam Format:	Multiple-choice
Exam Price:	USD 550
Real Exam Qty:	100
Exam Duration:	150 minutes
Sample Questions:	IAPP AIGP Sample Questions
Exam Way:	Online (OnVUE) or Test Center (Pearson VUE)
Pre Condition:	No specific prerequisites. Recommended background in privacy, compliance, legal, or technology.
Official Syllabus URL:	https://iapp.org/certify/aigp/

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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q28-Q33):

NEW QUESTION # 28
What is the primary purpose of an Al impact assessment?

A. Anticipate and manage the potential risks and harms of an Al system.
B. To define and evaluate the legal risks associated with developing an Al system.
C. To define and document the roles and responsibilities of Al stakeholders.
D. To identify and measure the benefits of an Al system.

Answer: A

Explanation:
The primary purpose of an AI impact assessment is to anticipate and manage the potential risks and harms of an AI system. This includes identifying the possible negative outcomes and implementing measures to mitigate these risks. This process helps ensure that AI systems are developed and deployed in a manner that is ethically and socially responsible, addressing concerns such as bias, fairness, transparency, and accountability.
The assessment often involves a thorough evaluation of the AI system's design, data inputs, outputs, and the potential impact on various stakeholders. This approach is crucial for maintaining public trust and adherence to regulatory requirements.

NEW QUESTION # 29
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 large organization least likely to use to responsibly train stakeholders on AI terminology, strategy and governance?

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

Answer: B

Explanation:
The correct answer is A. While educating technical staff is important, expecting all technical employees to be retooled as AI developers is unrealistic and not aligned with scalable governance practices.
From the AIGP ILT Guide:
"Training approaches should be role-specific and 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 Report 2024 supports 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 employees is the least practical and least likely approach.

NEW QUESTION # 30
You are a privacy program manager at a large e-commerce company that uses an Al tool to deliver personalized product recommendations based on visitors' personal information that has been collected from the company website, the chatbot and public data the company has scraped from social media.
A user submits a data access request under an applicable U.S. state privacy law, specifically seeking a copy of their personal data, including information used to create their profile for product recommendations.
What is the most challenging aspect of managing this request?

A. The data subject is not entitled to receive a copy of their data because some of it was scraped from public sources.
B. Some of the visitor's data is synthetic data that the company does not have to provide to the data subject.
C. The data subject's data is structured data that can be searched, compiled and reviewed only by an automated tool.
D. Some of the data subject's data is unstructured data and you cannot untangle it from the other data, including information about other individuals.

Answer: D

Explanation:
The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals.
Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.

NEW QUESTION # 31
What is the main purpose of accountability structures under the Govern function of the NIST AI Risk Management Framework?

A. To empower and train appropriate cross-functional teams.
B. To enable and encourage participation by external stakeholders.
C. To establish diverse, equitable and inclusive processes.
D. To determine responsibility for allocating budgetary resources.

Answer: A

Explanation:
Accountability structures under the NIST AI Risk Management Framework's Govern function focus on empowering and training cross-functional teams to manage AI risks effectively.

NEW QUESTION # 32
ISO/IEC 22989 and 42001 can be valuable resources for AI Governance professionals in all of the following ways EXCEPT:

A. Recommending key activities to assess and manage risk: test, evaluate, verify and validate (TEVV).
B. Establishing terminology and describing concepts so that governance team members can communicate with diverse parties and stakeholders from around the world.
C. Addressing specific issues related to managing procurement processes with third parties that provide or develop AI systems for their organization.
D. Being applicable to organizations of any size and industry seeking to use AI responsibly and effectively in their design processes, information systems and controls.

Answer: C

Explanation:
ISO/IEC 22989 and 42001 provide foundational concepts, terminology, and governance guidance for AI systems, but they do not address the detailed, specific processes required for managing procurement with third-party AI providers.

NEW QUESTION # 33
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