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IAPP Certified Artificial Intelligence Governance Professionalに合格するのに人気のあるAIGP合格問題 -多くの人におすすめ

ちなみに、JPNTest AIGPの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1Yx1vuvuO2WipwYm5c8By0bJS36DpWo3M

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JPNTestのAIGP試験参考書は他のAIGP試験に関連するする参考書よりずっと良いです。これは試験の一発合格を保証できる問題集ですから。この問題集の高い合格率が多くの受験生たちに証明されたのです。JPNTestのAIGP問題集は成功へのショートカットです。この問題集を利用したら、あなたは試験に準備する時間を節約することができるだけでなく、試験で楽に高い点数を取ることもできます。

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公認されたAIGP合格問題 | 最初の試行で簡単に勉強して試験に合格する & 最新のIAPP IAPP Certified Artificial Intelligence Governance Professional

IAPPのAIGPスタディガイドは無料のトライアルサービスを提供するため、スタディの内容、トピック、購入前にソフトウェアを最大限に活用する方法についての情報を得ることができます。どのようなAIGPテスト準備が適切であるかを選択し、不必要な無駄を避けるために適切な選択をするのに良い方法です。また、JPNTestのAIGP練習用トレントまたはトレイルプロセスの購入で問題が発生した場合は、すぐにご連絡いただければ、専門家によるAIGPのIAPP Certified Artificial Intelligence Governance Professional学習教材のオンラインサポートを提供します。

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 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 3

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 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 (Q121-Q126):

質問 # 121
An Al system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as?

A. Reliable.
B. Robust.
C. Reinforced.
D. Resilient.

正解：D

解説：
An AI system that maintains its level of performance within defined acceptable limits despite real-world or adversarial conditions is described as resilient. Resilience in AI refers to the system's ability to withstand and recover from unexpected challenges, such as cyber-attacks, hardware failures, or unusual input data. This characteristic ensures that the AI system can continue to function effectively and reliably in various conditions, maintaining performance and integrity. Robustness, on the other hand, focuses on the system's strength against errors, while reliability ensures consistent performance over time. Resilience combines these aspects with the capacity to adapt and recover.

質問 # 122
Which of the following disclosures is NOT required for an EU organization that developed and deployed a high-risk Al system?

A. The location(s) where data is stored.
B. The fact that an Al system is being used.
C. How an individual may contest a decision.
D. The human oversight measures employed.

正解：A

解説：
Under the EU AI Act, organizations that develop and deploy high-risk AI systems are required to provide several key disclosures to ensure transparency and accountability. These include the human oversight measures employed, how individuals can contest decisions made by the AI system, and informing individuals that an AI system is being used. However, there is no specific requirement to disclose the exact locations where data is stored. The focus of the Act is on the transparency of the AI system's operation and its impact on individuals, rather than on the technical details of data storage locations.

質問 # 123
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.
The frameworks that would be most appropriate for XYZ's governance needs would be the NIST Al Risk Management Framework and?

A. IEEE Ethical System Design Risk Management Framework (IEEE 7000-21).
B. Human Rights, Democracy, and Rule of Law Impact Assessment (HUDERIA).
C. NIST Cyber Security Risk Management Framework (CSF 2.0).
D. NIST Information Security Risk (NIST SP 800-39).

正解：A

解説：
The IEEE Ethical System Design Risk Management Framework (IEEE 7000-21) would be most appropriate for XYZ Corp's governance needs in addition to the NIST AI Risk Management Framework. The IEEE framework specifically addresses ethical concerns during system design, which is crucial for ensuring the responsible use of AI in hiring. It complements the NIST framework by focusing on ethical risk management, aligning well with XYZ Corp's goals of deploying AI responsibly and mitigating associated risks.

質問 # 124
Which of the following may be permissible uses of an AI system under the EU AI Act EXCEPT?

A. Detecting emotions in a telemedicine session
B. Using biometrics in abduction cases
C. Detecting emotions in a workplace for employee morale
D. Improving the response time for emergency services

正解：C

解説：
The correct answer is D. Emotion recognition in the workplace is flagged as unacceptable or highly restricted under the EU AI Act due to its intrusive nature and potential for misuse.
From the AIGP ILT Guide - EU AI Act Training Module:
"AI systems that monitor individuals' emotions in the workplace or educational settings are listed among prohibited or strictly limited practices under Article 5." AI Governance in Practice Report 2024 supports this interpretation:
"Emotion recognition systems, especially in sensitive contexts such as employment or education, raise significant concerns under EU fundamental rights law and are likely to be restricted." Other uses listed-such as emergency response or emotion detection in healthcare-may fall under lawful and beneficial uses, especially when justified by public interest.

質問 # 125
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.
The most significant risk from combining the healthcare network's existing data with the clinical research partner data is?

A. Security risk.
B. Privacy risk.
C. Operational risk.
D. Reputational risk.

正解：B

解説：
The most significant risk from combining the healthcare network's existing data with the clinical research partner data is privacy risk. Combining data sets, especially in healthcare, often involves handling sensitive information that could lead to privacy breaches if not managed properly. De-identified data can still pose re-identification risks when combined with other data sets. Ensuring privacy involves implementing robust data protection measures, maintaining compliance with privacy regulations such as HIPAA, and conducting thorough privacy impact assessments. Reference: AIGP Body of Knowledge on Data Privacy and Security.

質問 # 126
......

いまIAPPのAIGP認定試験に関連する優れた資料を探すのに苦悩しているのですか。もうこれ以上悩む必要がないですよ。ここにはあなたが最も欲しいものがありますから。受験生の皆さんの要望に答えるように、JPNTestはAIGP認定試験を受験する人々のために特に効率のあがる勉強法を開発しました。受験生の皆さんはほとんど仕事しながら試験の準備をしているのですから、大変でしょう。試験に準備するときにはあまり多くの時間を無駄にすることを避けるように、JPNTestは短時間の勉強をするだけで試験に合格することができるAIGP問題集が用意されています。この問題集には実際の試験に出る可能性のあるすべての問題が含まれています。従って、この問題集を真面目に学ぶ限り、AIGP認定試験に合格するのは難しいことではありません。

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