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完美的AIGP通過考試和資格考試中的領先優惠和實用的AIGP參考資料
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IAPP AIGP 考試大綱：
| 主題 |
簡介 |
| 主題 1 |
- Contemplating Ongoing Issues and Concerns: The topic focuses on issues around AI governance.
|
| 主題 2 |
- Understanding the AI Development Life Cycle: The topic outlines the context in which AI risks are managed.
|
| 主題 3 |
- Understanding How Current Laws Apply to AI Systems: It focuses on laws that govern the use of artificial intelligence.
|
>> AIGP通過考試 <<
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最新的 Artificial Intelligence Governance AIGP 免費考試真題 (Q62-Q67):
問題 #62
What is the most important reason to document the results of AI testing?
- A. To create a verifiable audit trail.
- B. To limit the need for future testing cycles.
- C. To identify areas for red-teaming focus.
- D. To support post-deployment maintenance.
答案：A
解題說明：
Testing results need to bedocumented thoroughlyto ensuretraceability, accountability, and compliance.
This is central to enabling audits, investigations, or regulatory inquiries into the system's development and performance.
From theAI Governance in Practice Report 2024:
"Documentation and recordkeeping are essential components... to demonstrate AI system compliance, trace system behavior, and support audits and conformity assessments." (p. 34-35)
"Maintaining audit trails across development and deployment enables transparency and accountability." (p.
12)
* AandBare benefits, but not theprimary governance justification.
* D- Limiting future testing is not a recommended goal.
問題 #63
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
Which stakeholder group is most important in selecting the specific type of algorithm?
- A. The cloud provider.
- B. The healthcare network's Al governance committee.
- C. The consulting firm.
- D. The healthcare network'sdata science team.
答案：D
解題說明：
In selecting the specific type of algorithm for the AI solution, the healthcare network's data science team is most important. This team possesses the technical expertise and understanding of the data, the clinical context, and the performance requirements needed to make an informed decision about which algorithm is most suitable. While the cloud provider and consulting firm can offer support and infrastructure, and the AI governance committee provides oversight, the data science team's specialized knowledge is crucial for selecting and implementing the appropriate algorithm. Reference: AIGP Body of Knowledge, AI governance and team roles section.
問題 #64
All of the following are reasons to deploy a challenger Al model in addition a champion Al model EXCEPT to?
- A. Automate real-time monitoring of the champion model.
- B. Perform testing on the champion model.
- C. Provide a framework to consider alternatives to the champion model.
- D. Retrain the champion model.
答案：D
解題說明：
Deploying a challenger AI model alongside a champion model is a strategy used to compare the performance of different models in a real-world environment. This approach helps in providing a framework to consider alternatives to the champion model, automating real-time monitoring of the champion model, and performing testing on the champion model. However, retraining the champion model is not a reason to deploy a challenger model. Retraining is a separate process that involves updating the champion model with new data or techniques, which is not related to the use of a challenger model.
Reference: AIGP BODY OF KNOWLEDGE, sections on model evaluation and management.
問題 #65
Scenario:
A U.S.-based AI governance professional is evaluating resources from the National Institute of Standards and Technology (NIST) to guide the organization's AI risk assessment strategy. They are particularly interested in programs focused on assessing AI-specific impacts.
The main purpose of NIST's Assessing Risks and Impacts of AI (ARIA) program is to:
- A. Pilot new standards for AI red-teaming
- B. Offer a regulatory sandbox for risk reporting
- C. Provide a suite of resources to manage risks
- D. Promote interoperability across AI systems
答案：C
解題說明：
The correct answer is A. The ARIA program by NIST is explicitly designed to support stakeholders in understanding and managing the risks and impacts of AI systems.
From the AIGP ILT Guide - U.S. Risk Frameworks Module:
"NIST's ARIA program develops and pilots assessment tools for AI risks and impacts, aimed at improving organizational capacity for responsible AI use." Also cited in the AI Governance in Practice Report 2024 (Frameworks Section):
"ARIA supports and aligns with the AI Risk Management Framework by helping organizations assess AI harms, safety concerns, and societal implications." ARIA is not a red-teaming or sandbox program-it's an assessment and governance resource.
問題 #66
What is the best method to proactively train an LLM so that there is mathematical proof that no specific piece of training data has more than a negligible effect on the model or its output?
- A. Transfer learning.
- B. Data compartmentalization.
- C. Differential privacy.
- D. Clustering.
答案：C
解題說明：
Differential privacy is a technique used to ensure that the inclusion or exclusion of a single data point does not significantly affect the outcome of any analysis, providing a way to mathematically prove that no specific piece of training data has more than a negligible effect on the model or its output. This is achieved by introducing randomness into the data or the algorithms processing the data. In the context of training large language models (LLMs), differential privacy helps in protecting individual data points while still enabling the model to learn effectively. By adding noise to the training process, differential privacy provides strong guarantees about the privacy of the training data.
Reference: AIGP BODY OF KNOWLEDGE, pages related to data privacy and security in model training.
問題 #67
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