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AIGP유효한덤프공부시험준비에가장좋은인기시험덤프
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IAPP AIGP 시험요강:
| 주제 |
소개 |
| 주제 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.
|
| 주제 2 |
- Understanding the Foundations of Artificial Intelligence: This topic defines AI and machine learning. It also provides an overview of the different types of AI systems and their use cases.
|
| 주제 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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IT업계에 종사하고 계신 분은IAPP AIGP 시험을 패스하여 자격증을 취득하려고 검색하다 저희 블로그를 보게 되시고 저희 사이트까지 방문하게 될것입니다. 방문하는 순간 IAPP AIGP시험에 대한 두려움이 사라질것입니다. 완벽한 구매후 서비스까지 겸비하고 있어 자격증을 취득하는데서의 믿음직스러운 동반자로 되어드릴게요.
최신 Artificial Intelligence Governance AIGP 무료샘플문제 (Q33-Q38):
질문 # 33
Which of the following is a subcategory of Al and machine learning that uses labeled datasets to train algorithms?
- A. Segmentation.
- B. Generative Al.
- C. Supervised learning.
- D. Expert systems.
정답：C
설명：
Supervised learning is a subcategory of AI and machine learning where labeled datasets are used to train algorithms. This process involves feeding the algorithm a dataset where the input-output pairs are known, allowing the algorithm to learn and make predictions or decisions based on new, unseen data. Reference:
AIGP BODY OF KNOWLEDGE, which describes supervised learning as a model trained on labeled data (e.
g., text recognition, detecting spam in emails).
질문 # 34
Scenario:
A public sector agency is reviewing proposed AI use cases for improving services. It wants to prioritize implementations that deliver value but minimize unintended negative consequences.
When evaluating which AI use cases to implement, an organization should consider all of the following EXCEPT:
- A. Related TEVV (test, evaluate, verify, validate) and system metrics
- B. Equitable access to the AI tool
- C. The users and their expectations
- D. Potential positive and negative impacts of the system
정답：A
설명：
The correct answer is A. While TEVV is important in later lifecycle phases, it is not the primary consideration when evaluating and prioritizing use cases.
From the AIGP Body of Knowledge - Use Case Assessment Module:
"Use case evaluation focuses on value, impact, fairness, and accessibility-technical testing considerations come later." ILT Guide confirms:
"Organizations should first assess whether the AI system provides equitable outcomes and aligns with stakeholder expectations. TEVV is part of implementation, not initial prioritization." Thus, A is not a top-level consideration during use case selection.
질문 # 35
Which risk management framework/guide/standard focuses on value-based engineering methodology?
- A. IEEE 7000-2021 Standard Model Process for Addressing Ethical Concerns during System Design.
- B. Council of Europe Human Rights, Democracy, and the Rule of Law Assurance Framework (HUDERIA) for Al Systems.
- C. ISO/IEC Guide 51 (Safety).
- D. ISO 31000 Guidelines (Risk Management).
정답：A
설명：
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.
질문 # 36
The processes and methods that allow human users to understand and trust the outputs produced by AI are important in addressing which key regulatory concern?
- A. Trustworthy AI
- B. Responsible AI
- C. Interpretable AI
- D. Explainable AI
정답：D
설명：
The correct answer is Explainable AI because it specifically refers to the ability of a system to describe the logic behind its decisions or outputs in a way that is understandable to humans. This is a key part of regulatory and ethical frameworks and is directly related to addressing the black-box problem in AI.
From the AIGP ILT Participant Guide (Module on Transparency and Explainability):
"Explainability refers to the understanding of how a black-box model works. The black-box problem exists because some models are too complex for human interpretation. Explainability methods aim to provide meaningful insight into the logic and decision-making of AI systems." Also, according to the AI Governance in Practice Report 2024:
"Explainability refers to the representation of the underlying mechanisms of the AI system's operation... a key tenet of AI governance due to the desire to understand how AI systems are built, managed and maintained." Thus, while Trustworthy and Responsible AI are broader concepts, explainability specifically targets the regulatory concern about understanding outputs.
질문 # 37
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model.
Which of the following is NOT a best practice apply during the testing?
- A. The test data should be anonymized to the extent practicable.
- B. Testing should be performed specific to the intended uses.
- C. The test data should be representative of the expected operationaldata.
- D. Testing should minimize human involvement to the extent practicable.
정답：D
설명：
Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model.
Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.
질문 # 38
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