AIGP認證題庫 - AIGP考證

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IAPP AIGP 考試大綱:
| 主題 | 簡介 |
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| 主題 1 | - 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.
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| 主題 2 | - 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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| 主題 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.
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| 主題 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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最新的 Artificial Intelligence Governance AIGP 免費考試真題 (Q25-Q30):
問題 #25
Retrieval-Augmented Generation (RAG) is defined as?
- A. Applying advanced filtering techniques to the LLMs.
- B. Reducing computational processing requirements of the LLMs.
- C. Combining LLMs with private knowledge bases to improve their outputs.
- D. Fine tuning LLMs to minimize biased outputs.
答案:C
解題說明:
Retrieval-Augmented Generation (RAG)enhances Large Language Models (LLMs) by integratingexternal, up-to-date, or proprietary informationinto the generation pipeline-allowing the model tofetch relevant factsfrom a trusted knowledge source at query time.
Though RAG is not defined directly in the IAPP documents, it is a widely recognized technique in AI governance for ensuringmore accurate and contextually grounded outputs, especially inregulated or high- stakes environmentswhere hallucinations are a concern.
* B, C, and Ddescribe optimization or bias mitigation-not the core function of RAG.
問題 #26
ISO 42001 International Standard offers guidance for organizations to develop trustworthy AI management systems by:
- A. Requiring organizations to continuously improve the effectiveness of their AI management systems.
- B. Requiring specific minimum parameters for key suppliers and key aspects of AI management systems.
- C. Focusing on high-risk aspects of development of AI management systems.
- D. Explicitly over-riding previously issued and now outdated ISO standards.
答案:A
解題說明:
ISO 42001 guides organizations to establish AI management systems with an emphasis on continuous improvement of their effectiveness to maintain trustworthiness.
問題 #27
Under the NIST AI Risk Management Framework, all of the following are defined as characteristics of trustworthy AI EXCEPT:
- A. Accountable and Transparent.
- B. Explainable and Interpretable.
- C. Tested and Effective.
- D. Secure and Resilient.
答案:C
問題 #28
What type of organizational risk is associated with AI's resource-intensive computing demands?
- A. Security risk.
- B. Third-party risk.
- C. Environmental risk.
- D. People risk.
答案:C
解題說明:
AI's resource-intensive computing demands contribute to increased energy consumption and carbon footprint, which are environmental risks for the organization.
問題 #29
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible Al?
- A. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.
- B. Employing a new software platform to modernize existing compliance processes across the organization.
- C. Consulting experts to consider the ethical principles underpinning the use of Al within the organization.
- D. Conducting an assessment of existing compliance programs to determine overlaps and integration points.
答案:B
解題說明:
When designing an integrated compliance strategy for responsible AI, the least likely step would be employing a new software platform to modernize existing compliance processes. While modernizing compliance processes is beneficial, it is not as directly related to the strategic integration of ethical principles and stakeholder concerns. More critical steps include conducting assessments of existing compliance programs to identify overlaps and integration points, consulting experts on ethical principles, and launching surveys to understand stakeholder concerns. These steps ensure that the compliance strategy is comprehensive and aligned with responsible AI principles. Reference: AIGP Body of Knowledge on AI Governance and Compliance Integration.
問題 #30
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