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The AIGP Certification Exam is one of the top-rated and career-oriented certificates that are designed to validate an IAPP professional's skills and knowledge level. These IAPP Certified Artificial Intelligence Governance Professional (AIGP) practice questions have been inspiring those who want to prove their expertise with the industrial-recognized credential. By cracking it you can gain several personal and professional benefits.

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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q193-Q198):

NEW QUESTION # 193
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?

Answer: D

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 # 194
CASE STUDY
Please use the following to 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.
To address these concerns, the company is considering using a third-party AI tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party AI-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 deploy technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by AI hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the AI hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
The frameworks that would be most appropriate for XYZ's governance needs would be the NIST AI Risk Management Framework and:

Answer: D

Explanation:
The IEEE Ethical System Design Risk Management Framework (IEEE 7000-21) complements the NIST AI Risk Management Framework by addressing ethical considerations in AI system design, fitting XYZ Corp's governance needs for responsible AI hiring tool use.


NEW QUESTION # 195
Scenario:
A distributor operating in the EU is responsible for selling imported high-risk AI systems to businesses. The distributor wants to ensure they fulfill all applicable obligations under the EU AI Act.
All of the following are obligations of a distributor of high-risk AI systems under the EU AI Act EXCEPT?

Answer: C

Explanation:
The correct answer isC.Registration in the EU databaseis an obligation ofprovidersof high-risk AI systems- not distributors.
From the AIGP ILT Guide - Roles & Obligations Module:
"Distributors must verify CE marking, ensure instructions for use are provided, inform authorities of risks, and take corrective action when necessary. However, registration duties in the EU database lie with the provider." Also from the AI Governance in Practice Report2025:
"The AI Act differentiates responsibilities for developers, providers, importers, and distributors. Only providers of high-risk systems are obligated to register their systems in the EU AI Database." Distributors focus onverification and communication, not formal registration.


NEW QUESTION # 196
A company that deploys AI but is not currently a provider or developer intends to develop and market its own AI system. Which obligation would then be likely to apply?

Answer: A

Explanation:
Once the company becomes a provider of an AI system, it becomes subject to the EU AI Act's provider obligations, the foundational one being the requirement to implement a formal risk- management framework for the system throughout its lifecycle.


NEW QUESTION # 197
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
The technology company has also addressed environmental concerns and societal harms.
Which of the following results would be considered biased outputs from this AI system EXCEPT?

Answer: B

Explanation:
The correct answer is A. Sending ads to construction companies (business entities) rather than individual workers is a business targeting decision, not inherently a biased AI output.
From the AIGP ILT Participant Guide - Bias & Fairness Module:
"Biased outputs often include stereotyping, exclusion of underrepresented groups, or reinforcing harmful societal assumptions." Examples like insufficient representation of minority groups or gender-stereotyping in visuals or language are typical manifestations of bias.
AI Governance in Practice Report 2024 also notes:
"Bias in generative models may manifest in representation gaps, stereotyping, or unequal performance across demographic groups." Option A, by contrast, describes a distribution strategy, not a bias generated by the AI model.


NEW QUESTION # 198
......

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