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IAPP AIGP Exam Syllabus Topics:

TopicDetails
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 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 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.

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100% Pass Quiz 2026 Perfect IAPP AIGP: Exam IAPP Certified Artificial Intelligence Governance Professional Sample

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q164-Q169):

NEW QUESTION # 164
CASE STUDY
Please use the following to answer the next question:
Good Values Corporation (GVC) is a U.S. educational services provider that employs teachers to create and deliver enrichment courses for high school students. GVC has learned that many of its teacher employees are using generative AI to create the enrichment courses, and that many of the students are using generative AI to complete their assignments.
In particular, GVC has learned that the teachers they employ used open source large language models ("LLM") to develop an online tool that customizes study questions for individual students.
GVC has also discovered that an art teacher has expressly incorporated the use of generative AI into the curriculum to enable students to use prompts to create digital art.
GVC has started to investigate these practices and develop a process to monitor any use of generative AI, including by teachers and students, going forward.
What is the best reason for GVC to offer students the choice to utilize generative AI in limited, defined circumstances?

Answer: D

Explanation:
Allowing limited use of generative AI helps students learn how to use AI responsibly and effectively as a supportive tool in their education.


NEW QUESTION # 165
A leading software development company wants to integrate AI-powered chatbots into their customer service platform. After researching various AI models in the market which have been developed by third-party developers, they're considering two options:
Option A - an open-source language model trained on a vast corpus of text data and capable of being trained to respond to natural language inputs.
Option B - a proprietary, generative AI model pre-trained on large data sets, which uses transformer-based architectures to generate human-like responses based on multimodal user input.
Option A would be the best choice for the company because?

Answer: C

Explanation:
Open-source modelsoffer morecustomization flexibility, allowing organizations to fine-tune or adapt the model tofit their own workflows, branding, or compliance needs- making it preferable when deep control is needed.
From theAI Governance in Practice Report 2024:
"Open-source AI allows organizations to review, adapt, and control model behavior in line with organizational needs and policies." (p. 39)


NEW QUESTION # 166
You are part of your organization's ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating. What is the best first step to address this?

Answer: C

Explanation:
Champion/challenger testing allows comparison between the current model and alternatives to identify if a replacement or adjustment is necessary, addressing accuracy deterioration systematically.


NEW QUESTION # 167
What is the most important reason for documenting risks when developing an AI system?

Answer: C

Explanation:
The most critical reason for documenting AI-related risks is toreduce exposure to legal, regulatory, and reputational liabilities. Clear documentation demonstrates thatrisks were identified, assessed, and addressed, which is essential for accountability and defensibility in the face of audits, litigation, or enforcement actions.
From theAI Governance in Practice Report 2024:
"An effective AI governance model is about collective responsibility... which should encompass oversight mechanisms such as privacy, accountability, compliance." (p. 13)
"Accountability... is based on the idea that there should be a person or entity that is ultimately responsible for any harm resulting from the use of the data, algorithm and AI system's underlying processes." (p. 28) While transparency, alignment with standards, and knowledge sharing are all secondary benefits,risk documentation's primary role is liability mitigation.


NEW QUESTION # 168
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
In the design phase, which of the following steps is most important in gathering the data from the clinical research partner?

Answer: B

Explanation:
Reviewing the terms of use is essential when gathering data from a clinical research partner. This step ensures that the healthcare network complies with all legal and contractual obligations related to data usage. It addresses data ownership, usage limitations, consent requirements, and privacy obligations, which are critical to maintaining ethical standards and avoiding legal repercussions. This review helps ensure that the data is used in a manner consistent with the agreements made and the regulatory environment, which is fundamental for lawful and ethical AI development. Reference: AIGP Body of Knowledge on Legal and Regulatory Considerations.


NEW QUESTION # 169
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