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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 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 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 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 (Q73-Q78):

NEW QUESTION # 73
Which of the following steps occurs in the design phase of the Al life cycle?

A. Performance evaluation.
B. Data augmentation.
C. Model explainability.
D. Risk impact estimation.

Answer: D

Explanation:
Risk impact estimation occurs in the design phase of the AI life cycle. This step involves evaluating potential risks associated with the AI system and estimating their impacts to ensure that appropriate mitigation strategies are in place. It helps in identifying and addressing potential issues early in the design process, ensuring the development of a robust and reliable AI system. Reference: AIGP Body of Knowledge on AI Design and Risk Management.

NEW QUESTION # 74
Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk AI system?

A. The fact that an AI system is being used.
B. The location(s) where data is stored.
C. How an individual may contest a decision.
D. The human oversight measures employed.

Answer: B

Explanation:
While transparency about AI use, human oversight, and contesting decisions are required disclosures for high-risk AI systems under the EU AI Act, the specific locations where data is stored are not mandated disclosures.

NEW QUESTION # 75
A US-based mortgage lender has purchased a chatbot. They plan to have the chatbot collect information from consumers who are interested in loans and offer the consumers 2-3 different options based on its current pricing and product offerings, which change frequently. This chatbot was initially developed and previously deployed by a Russian airline for booking flights.
The best option for the part of the process that generates the loan offers is?

A. Expert System.
B. Retrieval-Augmented Generation.
C. Quantum computing
D. Multimodal Generative AI.

Answer: A

Explanation:
Offeringloan products based on current offerings and rulesrequires a system that can followexplicit business logic, not generate open-ended content. Anexpert system, which is a rules-based AI that uses "if-then" logic, is ideal here.
From the AI governance context:
"Rule-based AI systems are often preferred when decisions must adhere to precise regulatory or financial criteria." (aligned with AI best practices in regulated sectors) A . RAGis used to integrate external knowledge-not suitable for structured, rule-based logic.
B . Multimodal modelshandle varied input types-not needed here.
D . Quantum computingis not yet practical or relevant for this business use case.

NEW QUESTION # 76
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. 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.
C. 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.
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 # 77
Machine learning is best described as a type of algorithm by which?

A. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
B. Systems can mimic human intelligence with the goal of performing routine tasks.
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 is defined as systems that automatically improve their performance through experience by identifying predictive patterns in data.

NEW QUESTION # 78
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