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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 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 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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q83-Q88):

NEW QUESTION # 83
CASE STUDY
Please use the following 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 Al to create the enrichment courses, and that many of the students are using generative Al 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 Al 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 Al, including by teachers and students, going forward.
What is the best reason for GVC to offer students the choice to utilize generative Al in limited, defined circumstances?

A. Toenable students to learn about practical applications of Al.
B. Toenable students to learn how to use Al as a supportive educational tool.
C. Toenable students to learn about performing research.
D. Toenable students to learn how to manage their time.

Answer: B

Explanation:
The best reason for GVC to offer students the choice to utilize generative AI in limited, defined circumstances is to enable students to learn how to use AI as a supportive educational tool. By integrating AI in a controlled manner, students can learn the practical applications of AI and develop skills to use AI responsibly and effectively in their educational pursuits.
Reference: The AIGP Body of Knowledge highlights the importance of teaching students about AI's practical applications and the responsible use of AI technologies. This aligns with the goal of fostering a better understanding of AI's role and its potential benefits in various contexts, including education.

NEW QUESTION # 84
A company developing and deploying its own AI model would perform all of the following steps to monitor and evaluate the model's performance EXCEPT:

A. Setting up automated tools to regularly track the model's accuracy, precision and recall rates in real-time.
B. Publicly disclosing data with forecasts of secondary and downstream harms to stakeholders.
C. Implementing a formal incident response plan to address incidents that may occur during system operation.
D. Establishing a regular schedule for human evaluation of the model's performance, including qualitative assessments.

Answer: B

Explanation:
Publicly disclosing data with forecasts of secondary and downstream harms is not a required or typical step in monitoring an AI model's performance. Monitoring focuses on internal tracking, incident response, and scheduled human review, not public disclosure of harm forecasts.

NEW QUESTION # 85
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?

A. GPU performance, to evaluate the tool ' s robustness.
B. Input data, to ensure the ads are reaching the target audience.
C. Algorithmic patterns, to show the model has a high degree of accuracy.
D. Output data, to assess the delta between the prediction and actual ad clicks.

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 # 86
Your organization is searching for a new way to help accurately forecast sales predictions by various types of customers.
Which of the following is the best type of model to choose if your organization wants to customize the model and avoid lock-in?

A. A subscription-based, multimodal model.
B. A free large language model.
C. A proprietary generative AI model.
D. A classic machine learning model.

Answer: D

Explanation:
Forcustomizable, interpretable modelsthat allow organizations toretain control and avoid vendor lock-in,classic ML models(e.g., regression, decision trees, random forests) are optimal.
From theAI Governance in Practice Report 2025:
"Organizations seeking transparency, customizability, and control often prefer classic ML models due to their flexibility and ease of governance." (p. 33) AandCmay have limited transparency and are often tied to specific providers.
Dinvolves ongoing costs and limited model control.

NEW QUESTION # 87
You are a privacy program manager at a large e-commerce company that uses an AI tool to deliver personalized product recommendations based on visitors' personal information that has been collected from the company website, the chatbot and public data the company has scraped from social media.
A user submits a data access request under an applicable US state privacy law, specifically seeking a copy of their personal data, including information used to create their profile for product recommendations.
What is the most challenging aspect of managing this request?

A. The data subject's data is structured data that can be searched, compiled and reviewed only by an automated tool.
B. Some of the data subject's data is unstructured data and you cannot untangle it from the other data, including information about other individuals.
C. Some of the visitor's data is synthetic data that the company does not have to provide to the data subject.
D. The data subject is not entitled to receive a copy of their data because some of it was scraped from public sources.

Answer: B

Explanation:
Unstructured data that cannot be easily separated from information about other individuals makes fulfilling the data access request complex while ensuring privacy for others.

NEW QUESTION # 88
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