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IAPP AIGP Exam Syllabus Topics:

Topic	Details
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 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 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 (Q126-Q131):

NEW QUESTION # 126
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. The aggregate count of user feedback:
flagged as Negative or Unsatisfactory over the past 30 days
categorized by language preference.
B. 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.
C. 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.
D. Monthly statistical measures showing:
the percentages of accuracy by user group
the response category
real world change data.

Answer: D

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 # 127
Machine learning is best described as a type of algorithm by which?

A. Systems can mimic human intelligence with the goal of replacing humans.
B. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
C. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
D. Systems can automatically improve from experience through predictive patterns.

Answer: D

Explanation:
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.

NEW QUESTION # 128
Your company is developing an AI system for global markets. This system involves personal data processing and some decision making that may affect rights of the individuals. Part of your role as an AI governance professional is to ensure the AI system complies with various international regulatory requirements. Which of the following approaches best aligns with a comprehensive understanding of and compliance with AI regulatory requirements in this scenario?

A. Develop a compliance strategy based on the strictest requirements in various regulations, including the EU AI Act, GDPR and HIPAA, and harmonize into a unified compliance framework.
B. Adopt a region-specific compliance strategy where the AI is modified to meet regulatory requirements of each region independently, disregarding commonalities or overlaps in international regulations.
C. Consider that if the AI system complies with local regulations of the country where the company is based, it is not necessary to comply with international regulations as AI is adaptable globally.
D. Focus on the EU AI Act as it is the most comprehensive regulation and its likely compliance with the act will ensure compliance with other international regulations that apply to AI.

Answer: A

NEW QUESTION # 129
According to the GDPR's transparency principle, when an Al system processes personal data in automated decision-making, controllers are required to provide data subjects specific information on?

A. The personal data used during processing, including inferences drawn by the Al system about the data.
B. The existence of automated decision-making and meaningful information on its logic and consequences.
C. The contact details of the data protection officer and the data protection national authority.
D. The data protection impact assessments carried out on the Al system and legal bases for processing.

Answer: B

Explanation:
The GDPR's transparency principle requires that when personal data is processed for automated decision- making, including profiling, data subjects must be informed about the existence of such automated decision- making. Additionally, they must be provided with meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for them. This requirement ensures that data subjects are fully aware of how their personal data is being used and the potential impacts, thereby promoting transparency and trust in the processing activities.

NEW QUESTION # 130
What is the most important factor when deciding whether or not to select a proprietary AI model?

A. Whether its system card identifies risks.
B. How frequently it will be updated.
C. What business purpose it will serve.
D. Whether its training data is disclosed.

Answer: C

Explanation:
Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.
From theAI Governance in Practice Report 2024:
"AI governance starts with defining the corporate strategy for AI... and aligning systems with business purpose and operational context." (p. 11)
* B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.

NEW QUESTION # 131
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