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AIGP模擬資料 & AIGP学習範囲

Fast2testは市場でテストされたすべてのAIGP浮き沈みを経験してきましたが、AIGP試験問題は完全にプロフェッショナルになりました。IAPP Certified Artificial Intelligence Governance Professional 最後に明るい光がある限り、道路で起こった困難を回避することはありません。それはあなたが望む満足のいく結果です。 知識の理論とクイズの問題の練習の両方がIAPP Certified Artificial Intelligence Governance Professional、試験に対処する際にあなたがより熟練するのに役立ちます。 当社の専門家は、AIGPすべての有用なコンテンツを統合することにより、IAPP試験の重要なポイントをトレーニング資料に抽出しました。

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.

>> AIGP模擬資料 <<

AIGP学習範囲、AIGP問題数

Fast2testのIAPPどのバージョンでも、IAPP Certified Artificial Intelligence Governance Professionalガイド資料はダウンロード数とAIGP同時ユーザー数に制限がないため、ユーザーは同じ質問セットで複数の演習を練習し、知識を繰り返し統合できます。 学習の過程で、IAPP Certified Artificial Intelligence Governance Professional実際の試験のテストエンジンは、学習プロセスの弱点を強化するのに便利です。 これは、間違ったAIGP質問を整理するプロセスの代替として使用できます

IAPP Certified Artificial Intelligence Governance Professional 認定 AIGP 試験問題 (Q67-Q72):

質問 # 67
Which of the following may be permissible uses of an AI system under the EU AI Act EXCEPT?

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

正解：D

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 # 68
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.
What is the best strategy to mitigate the bias uncovered in the loan applications?

• A. Procure a third-party statistical bias assessment tool.
• B. Retrain the model with data that reflects demographic parity.
• C. Document all instances of bias in the data set.
• D. Delete all gender-based data in the data set.

正解：B

解説：
Retraining the model with data that reflects demographic parity is the best strategy to mitigate the bias uncovered in the loan applications. This approach addresses the root cause of the bias by ensuring that the training data is representative and balanced, leading to more equitable decision-making by the AI model.
Reference: The AIGP Body of Knowledge stresses the importance of using high-quality, unbiased training data to develop fair and reliable AI systems. Retraining the model with balanced data helps correct biases that arise from historical inequalities, ensuring that the AI system makes decisions based on equitable criteria.

質問 # 69
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. How an individual may contest a decision.
• C. The fact that an Al system is being used.
• 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 # 70
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.
Each of the following steps would support fairness testing by the compliance team during the first month in production EXCEPT?

• A. Identifying if additional training data should be collected for specific demographic groups.
• B. Using tools to help understand factors that may account for differences in decision-making.
• C. Validating a similar level of decision-making across different demographic groups.
• D. Providing the loan applicants with information about the model capabilities and limitations.

正解：D

解説：
Providing the loan applicants with information about the model capabilities and limitations would not directly support fairness testing by the compliance team. Fairness testing focuses on evaluating the model's decisions for biases and ensuring equitable treatment across different demographic groups, rather than informing applicants about the model.
Reference: The AIGP Body of Knowledge outlines that fairness testing involves technical assessments such as validating decision-making consistency across demographics and using tools to understand decision factors. While transparency to applicants is important for ethical AI use, it does not contribute directly to the technical process of fairness testing.

質問 # 71
Which of the following elements of feature engineering is most important to mitigate the potential bias in an Al system?

• A. Feature importance analysis.
• B. Feature transformation.
• C. Feature selection.
• D. Feature validation.

正解：C

解説：
Feature selection is the most important element of feature engineering to mitigate potential bias in an AI system. This process involves choosing the most relevant and representative features from the data set, which directly affects the model's performance and fairness. By carefully selecting features, data scientists can reduce the influence of biased or irrelevant attributes, ensuring that the AI system is more accurate and equitable. Proper feature selection helps in eliminating biases that might stem from socio-demographic factors or other sensitive variables, leading to a more balanced and fair AI model. Reference: AIGP Body of Knowledge on Fairness in AI and Feature Engineering.

質問 # 72
......

IAPPお客様との持続可能な関係に高い価値を置いているため、AIGP準備ガイドのヘルプの下で最高の証明書学習体験をお楽しみいただけます。まず、5〜10分でお支払いが完了すると、短納期で、オンラインでAIGPガイドトレントをお送りします。加えて、当社のAIGP試験トレントの使用中に技術的および運用上の問題に対処するのに問題がある場合は、すぐにご連絡ください。24時間のオンラインサービスは、IAPP Certified Artificial Intelligence Governance Professional問題をすぐに解決するための努力です。

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