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Exam AIGP Discount | Latest AIGP Practice Materials

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Exam AIGP Discount | Latest AIGP Practice Materials

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IAPP AIGP Exam Syllabus Topics:
TopicDetails
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 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 4
  • 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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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q66-Q71):NEW QUESTION # 66
According to November 2023 White House Executive Order, which of the following best describes the guidance given to governmental agencies on the use of generative AI as a workplace tool?
  • A. Limit access to specific uses of generative AI.
  • B. Impose a general ban on the use of generative AI.
  • C. Impose a ban on the use of generative AI in agencies that protect national security.
  • D. Limit access of generative AI to engineers and developers.
Answer: A
Explanation:
Under the November 2023 Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, the Office of Management and Budget (OMB) is tasked with developing guidance for federal agencies on the responsible use of generative AI in the workplace. This guidance emphasizes the need for agencies to provide access to generative AI tools with appropriate safeguards, rather than imposing blanket restrictions or bans. The goal is to ensure that AI tools are used in a manner that protects Americans' rights and safety while promoting innovation and efficiency in government operations.

NEW QUESTION # 67
The planning phase of the Al life cycle articulates all of the following EXCEPT the?
  • A. Choice of the architecture.
  • B. Context in which the model will operate.
  • C. Approach to governance.
  • D. Objective of the model.
Answer: C
Explanation:
The planning phase of the AI life cycle typically includes defining the objective of the model, choosing the appropriate architecture, and understanding the context in which the model will operate. However, the approach to governance is usually established as part of the overall AI governance framework, not specifically within the planning phase. Governance encompasses broader organizational policies and procedures that ensure AI development and deployment align with legal, ethical, and operational standards. Reference: AIGP Body of Knowledge, AI lifecycle planning phase section.

NEW QUESTION # 68
Retraining an LLM can be necessary for all of the following reasons EXCEPT?
  • A. Adjust the model's hyper parameters to a specific use case.
  • B. To ensure interpretability of the model's predictions.
  • C. To minimize degradation in prediction accuracy due tochanges in data.
  • D. Account for new interpretations of the same data.
Answer: B
Explanation:
Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.

NEW QUESTION # 69
All of the following are reasons to deploy a challenger AI model in addition to a champion AI model EXCEPT to:
  • A. Automate real-time monitoring of the champion model.
  • B. Provide a framework to consider alternatives to the champion model.
  • C. Retrain the champion model.
  • D. Perform testing on the champion model.
Answer: C
Explanation:
Retraining the champion model is a maintenance activity, not a reason to deploy a challenger model; challenger models provide alternative options for comparison and testing.

NEW QUESTION # 70
The best method to ensure a comprehensive identification of risks for a new AI model is?
  • A. Integration testing.
  • B. Red teaming.
  • C. An environmental scan.
  • D. An impact assessment.
Answer: D
Explanation:
The most comprehensive way to identify a full range of risks - legal, ethical, operational, and societal - for a new AI model is through aformal impact assessment, such as aData Protection Impact Assessment (DPIA)orAlgorithmic Impact Assessment.
From theAI Governance in Practice Report 2024:
"Risk-based approaches are often distilled into organizational risk management efforts, which put impact assessments at the heart of deciding whether harm can be reduced." (p. 29)
"DPIAs... help organizations identify, analyze and minimize data-related risks and demonstrate accountability." (p. 30)
* A. Environmental scanis too general.
* B. Red teamingis useful for adversarial risk but not broad.
* C. Integration testingfocuses on technical/system compatibility, not overall risk.

NEW QUESTION # 71
......
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