IAPP AIGP資格トレーニング & AIGP資格認定試験

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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP (International Association of Privacy Professionals)
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional (AIGP) Exam
Exam Number:	AIGP
Exam Price:	USD 799 (non-member) / USD 649 (IAPP member)
Passing Score:	300 (scaled score out of 100–500)
Real Exam Qty:	100 (85 scored + ~15 unscored pilot questions)
Available Languages:	English
Certificate Validity Period:	2 years
Related Certifications:	CIPM CIPT CIPP/E CIPP/US
Exam Duration:	165 minutes
Exam Format:	Scenario-based questions, Multiple-choice, Multi-select, Single-select
Recommended Training:	AIGP Practice Exam (Official IAPP Store) IAPP Official AIGP Training and Resources
Exam Registration:	Official IAPP AIGP Exam Registration Pearson VUE Scheduling Portal (via IAPP account)
Sample Questions:	IAPP AIGP Sample Questions
Exam Way:	Computer-based exam delivered via Pearson VUE (test center or online proctored OnVUE)
Pre Condition:	None
Official Syllabus URL:	https://iapp.org/certify/aigp

>> IAPP AIGP資格トレーニング <<

AIGP資格認定試験、AIGP復習問題集

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IAPP AIGP 認定試験の出題範囲：

トピック	出題範囲
トピック 1	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.
トピック 2	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.
トピック 3	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.
トピック 4	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.

IAPP Certified Artificial Intelligence Governance Professional 認定 AIGP 試験問題 (Q159-Q164):

質問 # 159
Which of the following best defines an "Al model"?

A. A corpus of data which an Al algorithm analyzes to make predictions.
B. A system that applies defined rules to execute tasks.
C. A program that has been trained on a set of data to find patterns within the data.
D. A system of controls that is used to govern an Al algorithm.

正解：C

解説：
An AI model is best defined as a program that has been trained on a set of data to find patterns within that data. This definition captures the essence of machine learning, where the model learns from the data to make predictions or decisions. Reference: AIGP BODY OF KNOWLEDGE, which provides a detailed explanation of AI models and their training processes.

質問 # 160
The planning phase of the AI life cycle articulates all of the following EXCEPT the:

A. Context in which the model will operate.
B. Choice of the architecture.
C. Objective of the model.
D. Approach to governance.

正解：B

解説：
The choice of architecture is typically determined during the design phase, not the planning phase, which focuses on objectives, governance approach, and operational context.

質問 # 161
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
In cooperation with an Italian University, a US technology company called Nudge 2078, with no offices or employees in other countries, is developing an AI system which subtly encourages " good " behaviors, such as saving money and exercising. The two companies plan to conduct some experiments on volunteers. However, the study protocol is misleading on the purpose of the study, the experiments that will be conducted and its relevance to the application.
Which regulations are most likely applicable?

A. The EU AI Act.
B. The Digital Services Act.
C. Payment Card Industry PCI Standards.
D. The General Data Protection Act.
E. The Federal Trade Commission Act.

正解：A、D、E

解説：
The correct answers are A, C, and D because multiple regulatory regimes apply based on jurisdiction, data use, and deceptive practices. The EU AI Act applies because the system is being developed and tested in cooperation with an EU-based university and involves behavioral influence, which may fall under prohibited or high-risk AI practices, especially where manipulation or deception is present. The General Data Protection Regulation applies due to the processing of personal data of EU-based participants, particularly given the misleading study protocol, which violates principles such as transparency and informed consent. The Federal Trade Commission Act applies to the US company because deceptive or unfair practices, such as misleading participants about the purpose of experiments, fall under FTC enforcement. The Digital Services Act and PCI standards are not relevant to this specific use case.

質問 # 162
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Which of the following best describes data dependency with regard to an AI model?

A. Biases and limitations in an AI model often originate from biases and limitations present in its training data.
B. Using privacy-enhancing techniques is a core principle of data dependency in AI models.
C. Having large, high-quality datasets reduces data dependency in AI models.
D. The complexity and architecture of an AI model are influenced by the characteristics of its training data.
E. An AI model's performance depends on the quality, quantity, and diversity of its training data.

正解：A、D、E

解説：
AI models are intrinsically dependent on the data used to train, validate, and test them. NIST specifically identifies AI's inherent dependence on training data and links model reliability to data quality and representativeness. Therefore, A is correct. Training-data characteristics-including modality, structure, features, quantity, and complexity-also influence the appropriate model design and architecture, supporting D. E is correct because biases, gaps, nonrepresentative samples, and other limitations in training data can become embedded in model behavior and subsequently affect outputs. Privacy-enhancing technologies may reduce privacy risks, but they do not define data dependency, eliminating B. Likewise, improving dataset size and quality can improve performance but does not remove the model's fundamental dependence on data, so C is incorrect.

質問 # 163
CASE STUDY
Please use the following to 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 AI to create the enrichment courses, and that many of the students are using generative AI 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 AI 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 AI, including by teachers and students, going forward.
What is the best reason for GVC to offer students the choice to utilize generative AI in limited, defined circumstances?

A. To enable students to learn about practical applications of AI.
B. To enable students to learn how to manage their time.
C. To enable students to learn how to use AI as a supportive educational tool.
D. To enable students to learn about performing research.

正解：C

解説：
Allowing limited use of generative AI helps students learn how to use AI responsibly and effectively as a supportive tool in their education.

質問 # 164
......

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