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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
Related Certifications:	CIPM CIPT CIPP
Certificate Validity Period:	2 years
Exam Price:	USD 649 (members) / USD 799 (non-members)
Passing Score:	300 (scaled score out of 500)
Exam Format:	Scenario-based, Multiple-choice
Exam Duration:	165 (including 15-minute optional break)
Real Exam Qty:	100
Recommended Training:	Official AIGP Body of Knowledge & Study Guide IAPP Training & Resources
Exam Registration:	Pearson VUE Scheduling IAPP Official Registration
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 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 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 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 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 (Q160-Q165):

NEW QUESTION # 160
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 role-specific training, based on whether the organization uses a centralized, federated or decentralized governance model
B. Providing training on AI ethics, based on the extent to which the organization seeks to promote a responsible AI culture
C. Providing all technical employees education on AI development so they can retool and participate in the development of AI systems
D. Providing information and education to customers and users to understand the capabilities and limitations of the AI tools with which they interact

Answer: C

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 # 161
During the development of semi-autonomous vehicles, various failures occurred as a result of the sensors misinterpreting environmental surroundings, such as sunlight. These failures are an example of:

A. Hallucination.
B. Uncertainty.
C. Brittleness.
D. Forgetting.

Answer: C

Explanation:
Brittleness refers to AI systems failing unexpectedly when encountering slight variations or edge cases, such as sensor misinterpretation due to sunlight.

NEW QUESTION # 162
Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of Al systems?

A. National Institute of Standards and Technology (NIST).
B. National Science and Technology Council (NSTC).
C. Department of Homeland Security (DHS).
D. Office of Science and Technology Policy (OSTP).

Answer: A

Explanation:
The White House Executive Order of November 2023 designates the National Institute of Standards and Technology (NIST) as the responsible body for creating guidelines to conduct red-teaming tests of AI systems. NIST is tasked with developing and providing standards and frameworks to ensure the security, reliability, and ethical deployment of AI systems, including conducting rigorous red-teaming exercises to identify vulnerabilities and assess risks in AI systems.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and regulatory frameworks, and the White House Executive Order of November 2023.

NEW QUESTION # 163
The initial pilot effort for NIST's Assessing Risks and Impacts of AI (ARIA) Program is focused on risks associated with which of the following?

A. Recommender systems.
B. Facial recognition systems.
C. Text-to-image models.
D. Large language models.

Answer: D

Explanation:
The initial pilot effort of the NIST ARIA (Assessing Risks and Impacts of AI) Program focuses on evaluating risks and impacts associated with large language models (LLMs). This evaluation aims to assess how LLMs perform in real-world contexts, including their robustness, reliability, and potential societal impacts.

NEW QUESTION # 164
CASE STUDY
Please use the following answer the next question:
A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the publicsites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.
When notifying an accused perpetrator, what additional information should a police officer provide about the use of the Al system?

A. Information about the accuracy of the Al system.
B. Information about the composition of the training data of the system.
C. Information about how the individual was identified by the Al system.
D. Information about how the accused can oppose the charges.

Answer: C

Explanation:
When notifying an accused perpetrator, the police officer should provide information about how the individual was identified by the AI system. This transparency is crucial for maintaining trust and ensuring that the accused understands the basis of the charges against them. Information about the accuracy, how to oppose the charges, and the composition of the training data, while potentially relevant, do not directly address the immediate need for the accused to understand the specific process that led to their identification. Reference: AIGP Body of Knowledge on AI Transparency and Explainability.

NEW QUESTION # 165
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