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IAPP AIGP Exam Syllabus Topics:

TopicDetails
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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q139-Q144):

NEW QUESTION # 139
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?

Answer: B

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 # 140
You are an engineer that developed an AI-based ad recommendation tool.
Which of the following should be monitored to evaluate the tool's effectiveness?

Answer: D

Explanation:
The correct answer is A because evaluating the effectiveness of an AI system requires measuring how well its outputs align with real-world outcomes. In this case, comparing predicted ad engagement with actual user clicks provides a direct measure of model performance and business impact. AI governance frameworks emphasize outcome-based monitoring to ensure systems meet their intended objectives and deliver value. Tracking output performance also supports continuous improvement by identifying gaps between predictions and reality. Options B and C focus on indirect indicators, such as internal model behavior or input targeting, which do not fully capture effectiveness. Option D relates to infrastructure performance rather than model outcomes. Monitoring output accuracy and real-world results is essential for validating AI system effectiveness and ensuring accountability.


NEW QUESTION # 141
Scenario:
An organization is evaluating different AI models for integration into its internal workflows. Before moving forward with a particular AI solution from a third-party vendor, the governance team needs to assess the ethical and operational implications of the model.
The most important policy to assess the operations of an AI model is to follow the:

Answer: D

Explanation:
The correct answer isA. TheAcceptable Use Policy (AUP)sets the primary terms and conditions under which the AI model can be used, including limitations, prohibited uses, and operational constraints.
From the AIGP ILT Guide:
"The AUP is the most critical document to assess what you can and cannot do with the AI system. It governs use cases, outlines forbidden uses (e.g., disinformation), and supports responsible AI practices." Also confirmed in the AI Governance in Practice Report2025(Third-Party AI Assurance section):
"Reviewing and adhering to the acceptable use policy ensures that the model is being used in a manner that aligns with both provider expectations and ethical AI practices."


NEW QUESTION # 142
CASE STUDY
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 Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-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 Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
The organization continues planning the adoption of an AI tool to support hiring, but is concerned about potential bias in content generated by AI systems and how that could affect public perception.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?

Answer: C

Explanation:
Note: This is the same scenario and question as Question 21 and thus has the same correct answer: A. It's possible this was duplicated in your original input.
Repeated for clarity:
"Testing AI tools pre- and post-deployment helps ensure they perform as expected and do not introduce bias, privacy issues, or fairness concerns. This mitigates reputational and legal risk." The AI Governance in Practice Report 2024 further reinforces:
"Ongoing monitoring and testing post-deployment allows organizations to catch and correct unintended impacts... especially important in HR and hiring contexts."


NEW QUESTION # 143
The framework set forth in the White House Blueprint for an AI Bill of Rights addresses all of the following EXCEPT:

Answer: C

Explanation:
The White House's Blueprint for an AI Bill of Rights outlines five key principles to guide the design, use, and deployment of automated systems:
1. Safe and Effective Systems
2. Algorithmic Discrimination Protections
3. Data Privacy
4. Notice and Explanation
5. Human Alternatives, Consideration, and Fallback
It does not specifically include "High-risk mitigation standards" as a standalone principle. While the Blueprint emphasizes the importance of identifying and mitigating risks associated with automated systems, it does not explicitly label this as a separate principle.


NEW QUESTION # 144
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