AIGP認證考試的新考古題匯總

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IAPP AIGP 考試大綱：

主題	簡介
主題 1	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.
主題 2	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.
主題 3	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.
主題 4	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.

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最新的 Artificial Intelligence Governance AIGP 免費考試真題 (Q40-Q45):

問題 #40
Which stakeholder is responsible for lawful collection of data for the training of the foundational AI model?

A. The data aggregator.
B. The tech company.
C. The marketing agency's client.
D. The marketing agency.

答案：A

解題說明：
Data aggregators are third parties that collect and license data from various sources. They are responsible for ensuring thelawful collectionandproper usage rightsof the data they distribute - especially when such data is used to train foundational AI models.
From theAI Governance in Practice Report2025:
"As organizations have neither proximity to how third-party data was first collected nor direct control over the data governance practices of third parties, an organization can benefit from carrying out its own legal due diligence and third-party risk management." (p. 19)
"Legal due diligence may include verification of the personal data's lawful collection by the databroker..." (p.
19)
This confirms thatdata aggregatorsbear the legal and ethical burden to verify that data has been lawfully collected and is appropriately licensed for use, including in AI training.
* A. The marketing agencyandD. its clientmay use data, but they rely on upstream providers for its lawful origin.
* B. The tech companymay train the model but depends on lawful sourcing by data aggregators.

問題 #41
To maintain fairness in a deployed system, it is most important to:

A. Optimize computational resources and data to ensure efficiency and scalability.
B. Detect anomalies outside established metrics that require new training data.
C. Protect against loss of personal data in the model.
D. Monitor for data drift that may affect performance and accuracy.

答案：D

解題說明：
Monitoring for data drift is critical to maintain fairness, as shifts in data over time can degrade model performance and introduce bias.

問題 #42
A U.S. mortgage company developed an Al platform that was trained using anonymized details from mortgage applications, including the applicant's education, employment and demographic information, as well as from subsequent payment or default information. The Al platform will be used automatically grant or deny new mortgage applications, depending on whether the platform views an applicant as presenting a likely risk of default.
Which of the following laws is NOT relevant to this use case?

A. Title VII of the Civil Rights Act of 1964.
B. Fair Credit Reporting Act.
C. Fair Housing Act.
D. Equal Credit Opportunity Act.

答案：A

解題說明：
The U.S. mortgage company's AI platform relates to housing and credit, making the Fair Housing Act (A), Fair Credit Reporting Act (B), and Equal Credit Opportunity Act (C) relevant. Title VII of the Civil Rights Act of 1964 deals with employment discrimination and is not directly relevant to the mortgage application context (D).

問題 #43
All of the following may be permissible uses of an Al system under the EU Al Act EXCEPT?

A. To promote equitable distribution of welfare benefits.
B. To implement social scoring.
C. To detect an individual's intent for law enforcement purposes.
D. To manage border control.

答案：B

解題說明：
The EU AI Act explicitly prohibits the use of AI systems for social scoring by public authorities, as it can lead to discrimination and unfair treatment of individuals based on their social behavior or perceived trustworthiness. While AI can be used to promote equitable distribution of welfare benefits, manage border control, and even detect an individual's intent for law enforcement purposes (within strict regulatory and ethical boundaries), implementing social scoring systems is not permissible under the Act due to the significant risks to fundamental rights and freedoms.

問題 #44
All of the following are examples of biometric data in the US EXCEPT:

A. Iris scans.
B. GPS location of a user's fitness watch.
C. Walking gait.
D. Keystroke dynamics.

答案：B

解題說明：
GPS location data is not considered biometric data in the US. Biometric data involves measurements of physical or behavioral characteristics such as iris scans, gait, or keystroke dynamics.

問題 #45
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AIGP考試內容: https://www.kaoguti.com/AIGP_exam-pdf.html

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