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AIGPブレーン問題集でリアル試験最新問題2026年03月26日には166問題
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質問 # 20
Which of the following is an obligation of an importer of high-risk AI systems under the EU AI Act?
- A. Verify the Declaration of Conformity.
- B. Conduct a data protection impact assessment.
- C. Provide technical documentation.
- D. Affix the CE marking.
正解：A
解説：
Importers of high-risk AI systems into the EU havespecific responsibilitiesunder the EU AI Act. They arenot the parties responsible for affixing the CE marking or providing technical documentation-but they must verify that these have been done by the provider.
From theAI Governance in Practice Report 2024:
"Importers must verify that the appropriate conformity assessment has been carried out, the technical documentation is available, and the CE marking has been affixed." (p. 34-35) Thus:
* A. Provide technical documentation- done by theprovider.
* B. Affix the CE marking-provider'sresponsibility.
* C. Verify the Declaration of Conformity-importer obligation.
* D. Conduct a DPIA- relevant under data protection laws,not requiredunder the EU AI Act for importers.
質問 # 21
Scenario:
A financial services company is planning a new AI project to assess creditworthiness. The AI team is mapping out what tasks should be completed during the planning phase of the AI lifecycle.
The planning phase of the AI lifecycle includes all of the following EXCEPT:
- A. Approach to governance
- B. Choice of the architecture
- C. Context in which the model will operate
- D. Definition of underlying assumptions
正解：B
解説：
The correct answer is C. The choice of architecture (e.g., neural networks vs. decision trees) is typically part of the design and development phase, not the initial planning.
From the AIGP Body of Knowledge - AI Lifecycle Module:
"Planning involves scoping, context definition, stakeholder identification, governance planning, and assumptions-not yet model selection." Confirmed in the ILT Participant Guide:
"Design decisions such as architecture or algorithm type come after planning-usually during development based on technical feasibility and data availability."
質問 # 22
Which of the following most encourages accountability over Al systems?
- A. Determining the business objective and success criteria for the Al project.
- B. Understanding Al legal and regulatory requirements.
- C. Defining the roles and responsibilities of Al stakeholders.
- D. Performing due diligence on third-party Al training and testing data.
正解：C
解説：
Defining the roles and responsibilities of AI stakeholders is crucial for encouraging accountability over AI systems. Clear delineation of who is responsible for different aspects of the AI lifecycle ensures that there is a person or team accountable for monitoring, maintaining, and addressing issues that arise. This accountability framework helps in ensuring that ethical standards and regulatory requirements are met, and it facilitates transparency and traceability in AI operations. By assigning specific roles, organizations can better manage and mitigate risks associated with AI deployment and use.
質問 # 23
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. Conducting an assessment of existing compliance programs to determine overlaps and integration points.
- B. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.
- C. Consulting experts to consider the ethical principles underpinning the use of Al within the organization.
- D. Employing a new software platform to modernize existing compliance processes across the organization.
正解：D
解説：
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.
質問 # 24
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. Students must expressly consent to this use of generative Al.
- B. Content created by an LLM may be protectable under U.S. intellectual property law.
- C. Generative Al often creates content without attribution.
- D. Generative Al is generally trained using intellectual property owned by third parties.
正解：A
解説：
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.
質問 # 25
Which of the following is the least relevant consideration in assessing whether users should be given the right to opt out from an Al system?
- A. Industry practice.
- B. Risk to users.
- C. Feasibility.
- D. Cost of alternative mechanisms.
正解：D
解説：
When assessing whether users should be given the right to opt out from an AI system, the primary considerations are feasibility, risk to users, and industry practice. Feasibility addresses whether the opt-out mechanism can be practically implemented. Risk to users assesses the potential harm or benefits users might face if they cannot opt out. Industry practice considers the norms and standards within the industry. However, the cost of alternative mechanisms, while important in the broader context of implementation, is not directly relevant to the ethical consideration of whether users should have the right to opt out. The focus should be on protecting user rights and ensuring ethical AI practices.
Reference: AIGP BODY OF KNOWLEDGE, sections discussing user rights and ethical considerations in AI.
質問 # 26
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent.
The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
Which of the following measures should XYZ adopt to best mitigate its risk of reputational harm from using the Al tool?
- A. Direct the procurement team to select the most economical Al tool.
- B. Ensure the vendor assumes responsibility for all damages.
- C. Test the Al tool pre- and post-deployment.
- D. Continue to require XYZ's hiring personnel to manually screen all applicants.
正解：C
解説：
To mitigate the risk of reputational harm from using an AI hiring tool, XYZ Corp should rigorously test the AI tool both before and after deployment. Pre-deployment testing ensures the tool works correctly and does not introduce bias or other issues. Post-deployment testing ensures the tool continues to operate as intended and adapts to any changes in data or usage patterns. This approach helps to identify and address potential issues proactively, thereby reducing the risk of reputational harm. Ensuring the vendor assumes responsibility for damages (B) does not address the root cause of potential issues, selecting the most economical tool (C) may compromise quality, and continuing manual screening (D) defeats the purpose of using the AI tool.
質問 # 27
Which of the following is a foundational characteristic of effective AI governance?
- A. Engagement of a cross-functional team
- B. Reliance on tested vendor management processes
- C. Uniform policies and procedures across developer, deployer and user roles
- D. Thorough reviews of a company's public filings with experts
正解：A
解説：
The correct answer is Engagement of a cross-functional team. Effective AI governance requires collaboration among various organizational functions including legal, compliance, IT, ethics, and data science.
From the AIGP Body of Knowledge:
"AI governance cannot be siloed-it requires input and oversight from across departments... A cross- functional team ensures that ethical, technical, legal, and operational risks are all appropriately managed." Also confirmed in the ILT Participant Guide:
"Cross-functional teams allow organizations to bring in different perspectives... Legal, compliance, and technical experts must work together to ensure responsible AI outcomes."
質問 # 28
CASE STUDY
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address Al governance.
The marketing company has:
* Entered into a contract with the technology company with suitable representations and warranties.
* Completed an impact assessment on the LLM for this intended use.
* Built technical guidance on how to measure and mitigate bias in the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Followed applicable regulatory requirements.
* Created specific legal statements and disclosures regarding the use of the Al on its client's advertising.
The technology company has:
* Provided guidance and resources to developers to address environmental concerns.
* Build technical guidance on how to measure and mitigate bias in the LLM.
* Provided tools and resources to measure bias specific to the LLM.
* Enabled technical aspects of transparency, explainability, robustness and privacy.
* Mapped and mitigated potential societal harms and large-scale impacts.
* Followed applicable regulatory requirements and industry standards.
* Created specific legal statements and disclosures regarding the LLM. including with respect to IP and rights to data.
Which stakeholder is responsible for the lawful collection of data used to train the foundational AI model?
- A. The marketing agency
- B. The data aggregator
- C. The marketing agency's client
- D. The tech company
正解：D
解説：
The correct answer is B - The tech company. The party that develops and trains the foundational model is responsible for ensuring the lawful collection of training data.
From the AIGP ILT Guide - Foundational Models & Data Governance:
"Responsibility for the lawfulness of data collection typically lies with the party that trains the model- usually the provider or developer of the foundational model." AI Governance in Practice Report 2024 confirms:
"General Purpose AI providers are required to ensure that training data is lawfully acquired, including compliance with intellectual property and privacy requirements." The marketing agency is only a user or downstream integrator, not responsible for original data collection.
質問 # 29
According to the GDPR's transparency principle, when an Al system processes personal data in automated decision-making, controllers are required to provide data subjects specific information on?
- A. The personal data used during processing, including inferences drawn by the Al system about the data.
- B. The contact details of the data protection officer and the data protection national authority.
- C. The data protection impact assessments carried out on the Al system and legal bases for processing.
- D. The existence of automated decision-making and meaningful information on its logic and consequences.
正解：D
解説：
The GDPR's transparency principle requires that when personal data is processed for automated decision-making, including profiling, data subjects must be informed about the existence of such automated decision-making. Additionally, they must be provided with meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for them. This requirement ensures that data subjects are fully aware of how their personal data is being used and the potential impacts, thereby promoting transparency and trust in the processing activities.
質問 # 30
In procuring an AI system from a vendor, which of the following would be important to include in a contract to enable proper oversight and auditing of the system?
- A. Ownership of data and outputs.
- B. Responsibility for improvements.
- C. Appropriate access to data and models.
- D. Liability for mistakes.
正解：C
解説：
Ensuringoversight and auditabilityrequires that the organization hassufficient access to data, documentation, and model internalsor outputs necessary for evaluation.
From theAI Governance in Practice Report 2024:
"Access to technical documentation and system internals is essential to enable effective auditing, conformity checks, and accountability mechanisms." (p. 11, 34)
* Ais about liability, not auditability.
* Bmatters for IP rights, not oversight.
* Crelates to lifecycle responsibility but doesn't guarantee audit access.
質問 # 31
CASE STUDY
Please use the following answer the next question:
A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public sites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.
During the procurement process, what is the most likely reason that the third-party consultant asked each vendor for information about the diversity of their datasets?
- A. To determine the explainability of the Al system.
- B. To evaluate the reliability of the Al system.
- C. To assist the fairness of the Al system.
- D. To comply with applicable law.
正解：C
解説：
The third-party consultant asked each vendor for information about the diversity of their datasets to assist in ensuring the fairness of the AI system. Diverse datasets help prevent biases and ensure that the AI system performs equitably across different demographic groups. This is crucial for a law enforcement application, where fairness and avoiding discriminatory practices are of paramount importance. Ensuring diversity in training data helps in building a more just and unbiased AI system. Reference: AIGP Body of Knowledge on Ethical AI and Fairness.
質問 # 32
To maintain fairness in a deployed system, it is most important to?
- A. Monitor for data drift that may affect performance and accuracy.
- B. Optimize computational resources and data to ensure efficiency and scalability.
- C. Detect anomalies outside established metrics that require new training data.
- D. Protect against loss of personal data in the model.
正解：A
質問 # 33
Which of the following deployments of generative Al best respects intellectual property rights?
- A. The system produces content that is modified to closely resemble copyrightedwork.
- B. The system produces content that includes trademarks and copyrights.
- C. The system categorizes and applies filters to content based on licensing terms.
- D. The system provides attribution to creators of publicly available information.
正解：C
解説：
Respecting intellectual property rights means adhering to licensing terms and ensuring that generated content complies with these terms. A system that categorizes and applies filters based on licensing terms ensures that content is used legally and ethically, respecting the rights of content creators. While providing attribution is important, categorization and application of filters based on licensing terms are more directly tied to compliance with intellectual property laws. This principle is elaborated in the IAPP AIGP Body of Knowledge sections on intellectual property and compliance.
質問 # 34
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data-including applications, policies, and claims-and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed a human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?
- A. Toapply their own judgment to the initial assessment.
- B. Tosolicit on-going feedback on model performance.
- C. Toensure they provide transparency applicants on the model.
- D. Toprovide a reminder of a right appeal.
正解：A
解説：
Training underwriters on the model prior to deployment is crucial so they can apply their own judgment to the initial assessment. While AI models can streamline the process, human judgment is still essential to catch nuances that the model might miss or to account for any biases or errors in the model's decision-making process.
Reference: The AIGP Body of Knowledge emphasizes the importance of human oversight in AI systems, particularly in high-stakes areas such as underwriting and loan approvals. Human underwriters can provide a critical review and ensure that the model's assessments are accurate and fair, integrating their expertise and understanding of complex cases.
質問 # 35
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. Differential privacy.
- B. Transfer learning.
- C. Clustering.
- D. Data compartmentalization.
正解：A
解説：
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.
質問 # 36
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data-including applications, policies, and claims-and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed tA. human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
The best approach to enable a customer who wants information on the Al model's parameters for underwriting purposes is to provide?
- A. A transparency notice.
- B. An opt-out mechanism.
- C. Customer service support.
- D. Detailed terms of service.
正解：A
解説：
The best approach to enable a customer who wants information on the AI model's parameters for underwriting purposes is to provide a transparency notice. This notice should explain the nature of the AI system, how it uses customer data, and the decision-making process it follows. Providing a transparency notice is crucial for maintaining trust and compliance with regulatory requirements regarding the transparency and accountability of AI systems.
Reference: According to the AIGP Body of Knowledge, transparency in AI systems is essential to ensure that stakeholders, including customers, understand how their data is being used and how decisions are made. This aligns with ethical principles of AI governance, ensuring that customers are informed and can make knowledgeable decisions regarding their interactions with AI systems.
質問 # 37
The most important factor in ensuring fairness when training an Al system is?
- A. The data labeling and classification.
- B. The model accuracy and scale.
- C. The architecture and model selection.
- D. The data attributes and variability.
正解：D
解説：
Ensuring fairness when training an AI system largely depends on the data attributes and variability. This involves having a diverse and representative dataset that accurately reflects the population the AI system will serve. Fairness can be compromised if the data is biased or lacks variability, as the model may learn and perpetuate these biases. Diverse data attributes ensure that the model learns from a wide range of examples, reducing the risk of biased predictions. Reference: AIGP Body of Knowledge on Ethical AI Principles and Data Management.
質問 # 38
Scenario:
An organization is building a compliance program to ensure responsible AI deployment. It aims to align operations with AI risk frameworks and mitigate legal, ethical, and operational risks, while still promoting innovation.
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible AI?
- A. Employing a new software platform to modernize existing compliance processes across the organization
- B. Meeting with and obtaining approval from senior management
- C. Consulting experts to consider the ethical principles underpinning the use of AI within the organization
- D. Launching a survey to understand the concerns and interests of potentially impacted stakeholders
正解：A
解説：
The correct answer is D. While modernization through software may support efficiency, it is not a foundational or essential component of designing an integrated strategy.
From the AI Governance in Practice Report 2024:
"Integrated strategies rely on senior management support, ethical reviews, and stakeholder engagement... The use of tools and platforms may come later as an operational enhancement." Also confirmed in AIGP Body of Knowledge:
"Key components of a governance framework include leadership buy-in, ethical analysis, and stakeholder input. Tools are supporting elements-not strategic drivers."
質問 # 39
Which of the following steps occurs in the design phase of the Al life cycle?
- A. Performance evaluation.
- B. Model explainability.
- C. Data augmentation.
- D. Risk impact estimation.
正解：D
解説：
Risk impact estimation occurs in the design phase of the AI life cycle. This step involves evaluating potential risks associated with the AI system and estimating their impacts to ensure that appropriate mitigation strategies are in place. It helps in identifying and addressing potential issues early in the design process, ensuring the development of a robust and reliable AI system. Reference: AIGP Body of Knowledge on AI Design and Risk Management.
質問 # 40
A leading software development company wants to integrate AI-powered chatbots into their customer service platform. After researching various AI models in the market which have been developed by third-party developers, they're considering two options:
Option A - an open-source language model trained on a vast corpus of text data and capable of being trained to respond to natural language inputs.
Option B - a proprietary, generative AI model pre-trained on large data sets, which uses transformer-based architectures to generate human-like responses based on multimodal user input.
Option A would be the best choice for the company because?
- A. It is less expensive to run
- B. It is built for large-scale, complex dialogues and would be more effective in handling high-volume customer inquiries.
- C. It may be better suited for applications requiring customization.
- D. It can handle voice commands and is more suitable for phone-based customer support.
正解：C
解説：
Open-source modelsoffer morecustomization flexibility, allowing organizations to fine-tune or adapt the model tofit their own workflows, branding, or compliance needs- making it preferable when deep control is needed.
From theAI Governance in Practice Report 2024:
"Open-source AI allows organizations to review, adapt, and control model behavior in line with organizational needs and policies." (p. 39)
質問 # 41
What is the best reason for a company adopt a policy that prohibits the use of generative Al?
- A. Avoid the time necessary to train employees on acceptable use.
- B. Avoid accidental disclosure to its confidential and proprietary information.
- C. Avoid using technology that cannot be monetized.
- D. Avoid needing to identify and hire qualified resources.
正解：B
解説：
The primary concern for a company adopting a policy prohibiting the use of generative AI is the risk of accidental disclosure of confidential and proprietary information. Generative AI tools can inadvertently leak sensitive data during the creation process or through data sharing. This risk outweighs the other reasons listed, as protecting sensitive information is critical to maintaining the company's competitive edge and legal compliance. This rationale is discussed in the sections on risk management and data privacy in the IAPP AIGP Body of Knowledge.
質問 # 42
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