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IAPP AIGP Exam Overview:

Certification Vendor:IAPP
Exam Name:IAPP Certified Artificial Intelligence Governance Professional
Exam Number:AIGP
Real Exam Qty:100
Exam Duration:150 minutes
Certificate Validity Period:2 Years
Available Languages:English
Related Certifications:CIPP/A
CIPP/US
CIPM
CIPP/E
CIPT
Exam Price:USD 550
Passing Score:300
Exam Format:Multiple-choice
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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Additionally, all operating systems also support this format. The third format is the desktop AIGP Practice Exam software. It is ideal for users who prefer offline IAPP Certified Artificial Intelligence Governance Professional (AIGP) exam practice. This format is supported by Windows computers and laptops. You can easily install this software in your system to use it anytime to prepare for the examination.

IAPP AIGP Exam Syllabus Topics:

TopicDetails
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 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 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 (Q20-Q25):

NEW QUESTION # 20
Which of the following are subjects covered by a typical impact assessment?

Answer: C

Explanation:
Typical impact assessments cover fundamental rights, data protection, and safety to evaluate the ethical and legal implications of AI systems.


NEW QUESTION # 21
What is the technique to remove the effects of improperly used data from an ML system?

Answer: A

Explanation:
The correct answer is D, model disgorgement. This technique refers to removing or eliminating the influence of improperly obtained, biased, or unlawfully used data from a trained machine learning model. It is increasingly discussed in AI governance and regulatory enforcement, particularly where models have been trained on data collected without proper consent or in violation of legal requirements. Instead of merely cleaning datasets, model disgorgement may require retraining or discarding the model entirely to ensure that problematic data no longer influences outputs. This aligns with accountability and compliance principles in AI governance, where organizations must ensure lawful data use throughout the AI lifecycle. Other options like data cleansing and de-duplication address data quality but do not fully remove learned patterns already embedded in trained models.


NEW QUESTION # 22
According to November 2023 White House Executive Order, which of the following best describes the guidance given to governmental agencies on the use of generative AI as a workplace tool?

Answer: A

Explanation:
Under the November 2023 Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, the Office of Management and Budget (OMB) is tasked with developing guidance for federal agencies on the responsible use of generative AI in the workplace. This guidance emphasizes the need for agencies to provide access to generative AI tools with appropriate safeguards, rather than imposing blanket restrictions or bans. The goal is to ensure that AI tools are used in a manner that protects Americans' rights and safety while promoting innovation and efficiency in government operations.


NEW QUESTION # 23
Which of the following compliance related controls within an organization is most easily adapted to identify AI risks?

Answer: A

Explanation:
The correct answer is D because Privacy Impact Assessments are already structured processes designed to identify risks related to data use, processing, and potential harm to individuals. These assessments can be readily adapted to evaluate AI-specific risks, such as bias, automated decision-making impacts, and data protection concerns. AI governance frameworks emphasize leveraging existing compliance mechanisms to efficiently integrate AI risk management without duplicating processes. Privacy impact assessments align closely with AI risk evaluation because they examine how personal data is collected, used, and protected throughout the system lifecycle. Other options, such as penetration testing or training, focus on narrower objectives like security or awareness and are not comprehensive tools for identifying broader AI risks.
Adapting PIAs supports a risk-based, scalable, and governance-aligned approach to managing AI systems.


NEW QUESTION # 24
What is the most important factor when deciding whether or not to select a proprietary AI model?

Answer: A

Explanation:
The primary consideration in selecting a proprietary AI model is whether it effectively serves the intended business purpose.


NEW QUESTION # 25
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