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[General] AIGP Study Materials - AIGP New Practice Questions

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【General】 AIGP Study Materials - AIGP New Practice Questions

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IAPP AIGP Exam Syllabus Topics:
TopicDetails
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 (Q64-Q69):NEW QUESTION # 64
You are part of your organization's ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating.
What is the best first step address this?
  • A. Run red-teaming exercises.
  • B. Replace the model with a previous version.
  • C. Perform an audit of the model.
  • D. Conduct champion/challenger testing.
Answer: D
Explanation:
When the accuracy of a model deteriorates, the best first step is to conduct champion/challenger testing. This involves deploying a new model (challenger) alongside the current model (champion) to compare their performance. This method helps identify if the new model can perform better under current conditions without immediately discarding the existing model. It provides a controlled environment to test improvements and understand the reasons behind the deterioration. This approach is preferable to directly replacing the model, performing audits, or running red-teaming exercises, which may be subsequent steps based on the findings from the champion/challenger testing.
Reference: AIGP BODY OF KNOWLEDGE, sections on model performance management and testing strategies.

NEW QUESTION # 65
The OECD's Ethical Al Governance Framework is a self-regulation model that proposes to prevent societal harms by?
  • A. Establishing explain ability criteria to responsibly source and use data to train Al systems.
  • B. Focusing on Al technical design and post-deployment monitoring.
  • C. Defining requirements specific to each industry sector and high-risk Al domain.
  • D. Balancing Al innovation with ethical considerations.
Answer: D
Explanation:
The OECD's Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).

NEW QUESTION # 66
According to the Singapore Model Al Governance Framework, all of the following are recommended measures to promote the responsible use of Al EXCEPT?
  • A. Determining the level of human involvement in algorithmic decision-making.
  • B. Adapting the existing governance structure algorithmic decision-making.
  • C. Employing human-over-the-loop protocols for high-risk systems.
  • D. Establishing communications and collaboration among stakeholders.
Answer: C
Explanation:
The Singapore Model AI Governance Framework recommends several measures to promote the responsible use of AI, such as determining the level of human involvement in decision-making, adapting governance structures, and establishing communications and collaboration among stakeholders. However, employing human-over-the-loop protocols is not specifically mentioned in this framework. The focus is more on integrating human oversight appropriately within the decision-making process rather than exclusively employing such protocols. Reference: AIGP Body of Knowledge, section on AI governance frameworks.

NEW QUESTION # 67
A U.S. mortgage company developed an Al platform that was trained using anonymized details from mortgage applications, including the applicant's education, employment and demographic information, as well as from subsequent payment or default information. The Al platform will be used automatically grant or deny new mortgage applications, depending on whether the platform views an applicant as presenting a likely risk of default.
Which of the following laws is NOT relevant to this use case?
  • A. Fair Housing Act.
  • B. Fair Credit Reporting Act.
  • C. Equal Credit Opportunity Act.
  • D. Title VII of the Civil Rights Act of 1964.
Answer: D
Explanation:
The U.S. mortgage company's AI platform relates to housing and credit, making the Fair Housing Act (A), Fair Credit Reporting Act (B), and Equal Credit Opportunity Act (C) relevant. Title VII of the Civil Rights Act of 1964 deals with employment discrimination and is not directly relevant to the mortgage application context (D).

NEW QUESTION # 68
After initially deploying a third-party AI model, you learn the developer has released a new version.
As deployer of this third-party model, what should you do?
  • A. Retrain the model.
  • B. Communicate necessary updates to your users.
  • C. Audit the model.
  • D. Seek input from data scientists.
Answer: C
Explanation:
When anew versionof a third-party model is released, the deployer must ensure it still meets safety, performance, and compliance requirements - which calls for aformal audit.
From theAI Governance in Practice Report2025:
"Any updates or changes to AI systems should trigger a re-evaluation to ensure continued compliance and performance." (p. 12)
"Post-market monitoring includes reassessing the impact of updated models or retraining." (p. 35)

NEW QUESTION # 69
......
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