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| Topic |
Details |
| Topic 1 |
- Understanding AI Impacts and Responsible AI Principles: This topic identifies different risks that that ungoverned AI systems. The topic also describes features and principles that are essential for trustworthy and ethical AI.
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| Topic 2 |
- Implementing Responsible AI Governance and Risk Management: It explains the collaboration of major AI stakeholders in a layered approach.
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| Topic 3 |
- Understanding the Existing and Emerging AI Laws and Standards: This topic discusses global AI-specific laws such as the EU AI Act and Canada’s Bill C-27.
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| Topic 4 |
- Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q35-Q40):
NEW QUESTION # 35
What is the primary purpose of an Al impact assessment?
- A. To identify and measure the benefits of an Al system.
- B. To define and evaluate the legal risks associated with developing an Al system.
- C. Anticipate and manage the potential risks and harms of an Al system.
- D. To define and document the roles and responsibilities of Al stakeholders.
Answer: C
Explanation:
The primary purpose of an AI impact assessment is to anticipate and manage the potential risks and harms of an AI system. This includes identifying the possible negative outcomes and implementing measures to mitigate these risks. This process helps ensure that AI systems are developed and deployed in a manner that is ethically and socially responsible, addressing concerns such as bias, fairness, transparency, and accountability.
The assessment often involves a thorough evaluation of the AI system's design, data inputs, outputs, and the potential impact on various stakeholders. This approach is crucial for maintaining public trust and adherence to regulatory requirements.
NEW QUESTION # 36
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. Fines for SMEs and startups will be proportionally capped.
- C. Fines for violations of banned Al applications will be €35 million or 7% global annual turnover (whichever is higher).
- D. Rules on General Purpose Al will apply after 6 months as a specific provision.
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 # 37
CASE STUDY
Please use the following answer the next question:
Good Values Corporation (GVC) is a U.S. educational services provider that employs teachers to create and deliver enrichment courses for high school students. GVC has learned that many of its teacher employees are using generative Al to create the enrichment courses, and that many of the students are using generative Al to complete their assignments.
In particular, GVC has learned that the teachers they employ used open source large language models ("LLM") to develop an online tool that customizes study questions for individual students. GVC has also discovered that an art teacher has expressly incorporated the use of generative Al into the curriculum to enable students to use prompts to create digital art.
GVC has started to investigate these practices and develop a process to monitor any use of generative Al, including by teachers and students, going forward.
All of the following may be copyright risks from teachers using generative Al to create course content EXCEPT?
- A. Generative Al often creates content without attribution.
- B. Generative Al is generally trained using intellectual property owned by third parties.
- C. Content created by an LLM may be protectable under U.S. intellectual property law.
- D. Students must expressly consent to this use of generative Al.
Answer: D
Explanation:
All of the options listed may pose copyright risks when teachers use generative AI to create course content, except for students must expressly consent to this use of generative AI. While obtaining student consent is essential for ethical and privacy reasons, it does not directly relate to copyright risks associated with the creation and use of AI-generated content.
Reference: The AIGP Body of Knowledge discusses the importance of addressing intellectual property (IP) risks when using AI-generated content. Copyright risks are typically associated with the use of third-party data and the lack of attribution, rather than the consent of users.
NEW QUESTION # 38
When monitoring the functional performance of a model that has been deployed into production, all of the following are concerns EXCEPT?
- A. Model drift.
- B. Data loss.
- C. Feature drift.
- D. System cost.
Answer: D
Explanation:
When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model's predictions. Model drift is when the model's performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.
NEW QUESTION # 39
Retraining an LLM can be necessary for all of the following reasons EXCEPT?
- A. To minimize degradation in prediction accuracy due tochanges in data.
- B. Account for new interpretations of the same data.
- C. Adjust the model's hyper parameters specific use case.
- D. To ensure interpretability of the model's predictions.
Answer: D
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 # 40
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