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IAPP AIGP Exam Syllabus Topics:

Topic	Details
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 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 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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q103-Q108):

NEW QUESTION # 103
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.
All of the following may be copyright risks from teachers using generative AI to create course content EXCEPT:

A. Content created by an LLM may be protectable under U.S. intellectual property law.
B. Students must expressly consent to this use of generative AI.
C. Generative AI often creates content without attribution.
D. Generative AI is generally trained using intellectual property owned by third parties.

Answer: B

Explanation:
Student consent is not typically a copyright risk related to teachers using generative AI; the other options highlight key copyright concerns around AI-generated content and data training sources.

NEW QUESTION # 104
Scenario:
A company is using different types of AI systems to enhance consumer engagement. These include chatbots, recommendation engines, and automated content generation tools.
Which of the following situations would be least likely to raise concerns under existing consumer protection laws?

A. An AI tool using scraped digital content to generate news summaries on a publishing website
B. An online platform offering recommendations to its users by displaying user-specific content and targeted advertisements
C. An AI customer service system claiming that it is as accurate as a human support agent
D. An AI algorithm being used in a credit decision-making process by a financial institution

Answer: B

Explanation:
The correct answer is D. Personalized content and advertisements, as long as properly disclosed and non- deceptive, are not generally a consumer protection issue under current legal regimes.
From the AI Governance in Practice Report 2024 (Consumer Protection Section):
"Standard practices like targeted advertising and recommendations are widely accepted provided they comply with transparency and consent requirements." Meanwhile, credit decision-making and misleading AI performance claims (Answers A and B) have already led to regulatory enforcement.
The AIGP ILT Guide highlights:
"Deceptive claims, biased financial decisions, and unauthorized data use may violate consumer protection and privacy laws. Advertising personalization is routine but must be disclosed appropriately."

NEW QUESTION # 105
What is a primary liability concern when a company deploys a proprietary Agentic AI model?

A. Lack of available APIs for memory integration.
B. Dependence on open-source software for orchestration.
C. Direct accountability for harmful or deceptive agent behavior.
D. Greater public scrutiny due to external model hosting.

Answer: C

Explanation:
The correct answer is A because deploying a proprietary agentic AI model places direct responsibility on the organization for the actions and outcomes of that system. AI governance frameworks emphasize accountability as a core principle, meaning organizations must take responsibility for harms caused by their AI systems, including misleading, biased, or unsafe outputs. Agentic AI systems, which can act autonomously and make decisions, introduce heightened liability risks because their behavior may directly impact users or third parties. Regulatory approaches increasingly focus on ensuring that deployers cannot shift responsibility, especially when the system is proprietary and under their control. The other options relate to operational or technical considerations, not primary legal liability. Ensuring accountability for AI behavior is central to compliance, risk management, and maintaining trust.

NEW QUESTION # 106
Training data is best defined as a subset of data that is used to:

A. Resemble the structure and statistical properties of production data.
B. Detect the initial sources of biases to mitigate prior to deployment.
C. Enable a model to detect and learn patterns.
D. Fine-tune a model to improve accuracy and prevent overfitting.

Answer: C

Explanation:
Training data is the dataset used to enable a machine learning model to detect and learn underlying patterns, which forms the basis for its predictive capabilities.

NEW QUESTION # 107
CASE STUDY
Please use the following to answer the next question:
You have recently assumed the role of AI Governance leader for a California-based medical technology company. The organization primarily serves hospitals and has recently expanded to include walk-in clinics located within local pharmacies.
The company ' s core business focuses on diagnostic assistance powered by a large language model LLM and back-office process optimization using Agentic AI, including chatbots, medical record request handling, scheduling and billing.
In preparation for its next round of funding, the board has asked you to prepare an AI Risk report to demonstrate to investors how the company is addressing AI-related risks. In preparing the report you learn that last year the company generated 30 million dollars in gross revenue across the US, EU, India, and South Korea and that vendors are engaged for various activities, including model testing and providing third-party AI solutions for chatbots.
Which of the following would provide you the best information addressing quality principles pertaining to the functioning of the AI agents and LLM?

A. Monthly statistical measures showing:
the percentages of accuracy by user group
the response category
real world change data.
B. Real-time system diagnostics tracking:
the total number of model predictions processed daily
the percentage of high-certainty predictions
a summary of code quality scores from internal software testing tools before deployment.
C. A monthly log of all input data validation checks showing:
the percentage of anomalous or missing data points that were cleaned
the average time it takes for the LLM to generate a response.
D. The aggregate count of user feedback:
flagged as Negative or Unsatisfactory over the past 30 days
categorized by language preference.

Answer: A

Explanation:
The correct answer is D because it directly reflects core data and model quality principles such as accuracy, performance consistency, and real-world effectiveness across different user groups. AI governance frameworks emphasize that quality must be evaluated based on whether outputs are accurate, complete, and fit for purpose in real-world conditions. Measuring accuracy by user group also supports fairness and bias detection, which are essential components of trustworthy AI. Option D captures outcome-based performance and aligns with continuous monitoring expectations across the AI lifecycle. In contrast, options A and C focus more on operational or technical metrics, while B reflects user sentiment rather than objective quality.
According to AI governance principles, high-quality AI systems require ongoing evaluation of outputs against real-world results to ensure reliability, validity, and safe deployment.

NEW QUESTION # 108
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