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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP (International Association of Privacy Professionals)
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional Exam
Exam Number:	AIGP
Available Languages:	English
Certificate Validity Period:	2 years
Exam Duration:	165 (including 15-minute optional break)
Exam Format:	Scenario-based, Multiple-choice
Related Certifications:	CIPM CIPT CIPP
Passing Score:	300 (scaled score out of 500)
Real Exam Qty:	100
Exam Price:	USD 649 (members) / USD 799 (non-members)
Recommended Training:	Official AIGP Body of Knowledge & Study Guide IAPP Training & Resources
Exam Registration:	IAPP Official Registration Pearson VUE Scheduling
Sample Questions:	IAPP AIGP Sample Questions
Exam Way:	Online remote proctored or in-person at Pearson VUE test centers
Pre Condition:	No formal prerequisites; open to all professionals
Official Syllabus URL:	https://iapp.org/certify/aigp

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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 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 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q118-Q123):

NEW QUESTION # 118
Which of the following most encourages accountability over Al systems?

A. Performing due diligence on third-party Al training and testing data.
B. Understanding Al legal and regulatory requirements.
C. Determining the business objective and success criteria for the Al project.
D. Defining the roles and responsibilities of Al stakeholders.

Answer: D

Explanation:
Defining the roles and responsibilities of AI stakeholders is crucial for encouraging accountability over AI systems. Clear delineation of who is responsible for different aspects of the AI lifecycle ensures that there is a person or team accountable for monitoring, maintaining, and addressing issues that arise. This accountability framework helps in ensuring that ethical standards and regulatory requirements are met, and it facilitates transparency and traceability in AI operations. By assigning specific roles, organizations can better manage and mitigate risks associated with AI deployment and use.

NEW QUESTION # 119
Which of the following use cases would be best served by a non-AI solution?

A. A customer service agency wants to automate answers to common questions.
B. An e-commerce provider wants to make personalized recommendations.
C. A business analyst wants to develop advertising campaigns.
D. A non-profit wants to develop a social media presence.

Answer: D

Explanation:
The correct answer is A because developing a social media presence typically does not require AI and can be effectively achieved through traditional digital marketing strategies, content planning, and manual engagement. AI governance principles emphasize that organizations should first determine whether AI is necessary or proportionate to the problem being solved. Using AI where simpler, deterministic, or human-driven solutions suffice can introduce unnecessary complexity, cost, and risk. In contrast, options B, C, and D involve tasks such as campaign optimization, personalization, and automation, which benefit significantly from AI capabilities like pattern recognition, predictive analytics, and natural language processing. Responsible AI governance encourages a "fit-for-purpose" approach, ensuring AI is only deployed when it provides clear added value over non-AI alternatives.

NEW QUESTION # 120
The OECD's Ethical Al Governance Framework is a self-regulation model that proposes to prevent societal harms by?

A. Balancing Al innovation with ethical considerations.
B. Establishing explain ability criteria to responsibly source and use data to train Al systems.
C. Focusing on Al technical design and post-deployment monitoring.
D. Defining requirements specific to each industry sector and high-risk Al domain.

Answer: A

Explanation:
The OECD's Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).

NEW QUESTION # 121
What is the primary objective of continuous monitoring in the lifecycle of an AI tool?

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

Answer: C

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 # 122
A company developed Al technology that can analyze text, video, images and sound to tag content, including the names of animals, humans and objects.
What type of Al is this technology classified as?

A. Multi-modal model.
B. Deductive inference.
C. Transformative Al.
D. Expert system.

Answer: A

Explanation:
A multi-modal model is an AI system that can process and analyze multiple types of data, such as text, video, images, and sound. This type of AI integrates different data sources to enhance its understanding and decision-making capabilities. In the given scenario, the AI technology that tags content including names of animals, humans, and objects falls under this category. Reference: AIGP BODY OF KNOWLEDGE, which outlines the capabilities and use cases of multi-modal models.

NEW QUESTION # 123
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