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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 (Q94-Q99):

NEW QUESTION # 94
Which risk management framework/guide/standard focuses on value-based engineering methodology?

A. ISO 31000 Guidelines (Risk Management).
B. IEEE 7000-2021 Standard Model Process for Addressing Ethical Concerns during System Design.
C. ISO/IEC Guide 51 (Safety).
D. Council of Europe Human Rights, Democracy, and the Rule of Law Assurance Framework (HUDERIA) for Al Systems.

Answer: B

Explanation:
The IEEE 7000-2021 Standard focuses on a value-based engineering methodology for addressing ethical concerns during system design. This standard guides engineers and organizations in integrating ethical considerations into the design and development processes of AI systems, ensuring that these technologies are developed responsibly and align with human values. Reference: AIGP Study Material, section on risk management frameworks and standards.

NEW QUESTION # 95
After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?

A. Define a model-validation methodology.
B. Identify known edge cases to monitor post-deployment.
C. Document maintenance teams and processes.
D. Perform a readiness assessment.

Answer: D

Explanation:
After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance.
It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.

NEW QUESTION # 96
Scenario:
A mid-sized tech firm is building its AI governance program and is exploring ISO/IEC standards that could support consistency in terminology and risk assessment processes across teams.
ISO/IEC 22989 and ISO/IEC 42001 can be valuable resources for AI Governance professionals in all of the following ways EXCEPT:

A. Recommending key activities to assess and manage risk: test, evaluate, verify and validate (TEVV)
B. Establishing terminology and describing concepts so that governance team members can communicate with diverse parties and stakeholders from around the world
C. Addressing specific issues related to managing procurement processes with third parties that provide or develop AI systems for their organization
D. Being applicable to organizations of any size and industry seeking to use AI responsibly and effectively in their design processes, information systems and controls

Answer: C

Explanation:
The correct answer is C. ISO/IEC 22989 and 42001 focus on terminology, risk, and management systems, but do not specifically address procurement-related concerns with third-party vendors.
From the AIGP Body of Knowledge - Standards Section:
"ISO/IEC 22989 defines terminology and foundational concepts. ISO/IEC 42001 provides a management system standard for AI. They are not procurement-focused documents." Also confirmed in the AI Governance in Practice Report 2024:
"These standards help establish common language and risk governance procedures. Procurement governance typically falls under separate frameworks or sector-specific guidance." Thus, procurement governance (Option C) is not a central use case for these standards.

NEW QUESTION # 97
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
The technology company has also addressed environmental concerns and societal harms.
Which of the following results would be considered biased outputs from this AI system EXCEPT?

A. The advertising text generated for female audiences focuses on color and style
B. The images of female workers are hyper-sexualized
C. The generated ads are sent to construction companies, not individual workers
D. The content generated for minority construction workers is insufficient

Answer: C

Explanation:
The correct answer is A. Sending ads to construction companies (business entities) rather than individual workers is a business targeting decision, not inherently a biased AI output.
From the AIGP ILT Participant Guide - Bias & Fairness Module:
"Biased outputs often include stereotyping, exclusion of underrepresented groups, or reinforcing harmful societal assumptions." Examples like insufficient representation of minority groups or gender-stereotyping in visuals or language are typical manifestations of bias.
AI Governance in Practice Report 2024 also notes:
"Bias in generative models may manifest in representation gaps, stereotyping, or unequal performance across demographic groups." Option A, by contrast, describes a distribution strategy, not a bias generated by the AI model.

NEW QUESTION # 98
All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?

A. The Al Pact will act as a transitional bridge until the Regulations are fully enacted.
B. Rules on General Purpose Al will apply after 6 months as a specific provision.
C. Fines for violations of banned Al applications will be €35 million or 7% global annual turnover (whichever is higher).
D. Fines for SMEs and startups will be proportionally capped.

Answer: A

Explanation:
The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as €35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an "AI Pact" acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.

NEW QUESTION # 99
......

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