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[General] 試験の準備方法-検証するAIF-C01資格認定試験-真実的なAIF-C01模擬資料

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【General】 試験の準備方法-検証するAIF-C01資格認定試験-真実的なAIF-C01模擬資料

Posted at yesterday 12:13      View：4 | Replies：0        Print      Only Author   [Copy Link] 1#
無料でクラウドストレージから最新のCertJuken AIF-C01 PDFダンプをダウンロードする：https://drive.google.com/open?id=1fY-5wIc8GnkhbfJdTJ_QgCQlnFtBC91U
人はそれぞれの夢を持っています。あなたの夢は何でしょうか。昇進ですか。あるいは高給ですか。私の夢はAmazonのAIF-C01認定試験に受かることです。この認証の証明書を持っていたら、全ての難問は解決できるようになりました。この試験に受かるのは難しいですが、大丈夫です。私はCertJukenのAmazonのAIF-C01試験トレーニング資料を選びましたから。私が自分の夢を実現することを助けられますから。あなたもITに関する夢を持っていたら、速くCertJukenのAmazonのAIF-C01試験トレーニング資料を選んでその夢を実現しましょう。CertJukenは絶対信頼できるサイトです。
Amazon AIF-C01 認定試験の出題範囲：
トピック出題範囲
トピック 1
  • Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
トピック 2
  • Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
トピック 3
  • Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.
トピック 4
  • Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
トピック 5
  • Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.

試験の準備方法-ハイパスレートのAIF-C01資格認定試験-更新するAIF-C01模擬資料当社のAIF-C01実践教材は一流の専門家によって編集され、AIF-C01スタディガイドは思いやりのあるサービスとアクセス可能なコンテンツのパッケージ全体を提供します。 さらに、AIF-C01 Actual Testは、さまざまな側面で効率を改善します。 専門的な知識を十分に身に付けることは、あなたの人生に大いに役立ちます。 知識の時代の到来により、私たちはすべて、AIF-C01などの専門的な証明書を必要としています。
Amazon AWS Certified AI Practitioner 認定 AIF-C01 試験問題 (Q350-Q355):質問 # 350
Which task represents a practical use case to apply a regression model?
  • A. Create a picture that shows a specific object.
  • B. Cluster movies based on movie ratings and viewers.
  • C. Use historical data to predict future temperatures in a specific city.
  • D. Suggest a genre of music for a listener from a list of genres.
正解：C
解説：
* Regression predicts continuous numerical values (e.g., stock prices, temperatures).
* A is classification (genre selection).
* B is clustering.
* D is generative AI/computer vision.
# Reference:
AWS ML Glossary - Regression

質問 # 351
Which component of Amazon Bedrock Studio can help secure the content that AI systems generate?
  • A. Guardrails
  • B. Access controls
  • C. Function calling
  • D. Knowledge bases
正解：A
解説：
Amazon Bedrock Studio provides tools to build and manage generative AI applications, and the company needs a component to secure the content generated by AI systems. Guardrails in Amazon Bedrock are designed to ensure safe and responsible AI outputs by filtering harmful or inappropriate content, making them the key component for securing generated content.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Guardrails in Amazon Bedrock provide mechanisms to secure the content generated by AI systems by filtering out harmful or inappropriate outputs, such as hate speech, violence, or misinformation, ensuring responsible AI usage." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Option A: Access controlsAccess controls manage who can use or interact with the AI system but do not directly secure the content generated by the system.
Option B: Function callingFunction calling enables AI models to interact with external tools or APIs, but it is not related to securing generated content.
Option C: GuardrailsThis is the correct answer. Guardrails in Amazon Bedrock secure generated content by filtering out harmful or inappropriate material, ensuring safe outputs.
Option D: Knowledge basesKnowledge bases provide data for AI models to generate responses but do not inherently secure the content that is generated.
Reference:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedr ... ide/guardrails.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Securing AI Outputs (https://aws.amazon.com/bedrock/)

質問 # 352
A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.
Which AWS service or feature meets these requirements?
  • A. Amazon SageMaker JumpStart
  • B. Amazon SageMaker Data Wrangler
  • C. Amazon SageMaker Model Monitor
  • D. Amazon SageMaker HyperPod
正解：C
解説：
Amazon SageMaker Model Monitor is specifically designed to automatically detect and alert on changes in model quality, such as data drift, prediction drift, or other anomalies in model performance once deployed.
D is correct:
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions." (Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide)
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions." (Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide) A (JumpStart) provides prebuilt solutions and models, not monitoring.
B (HyperPod) is for large-scale training, not model monitoring.
C (Data Wrangler) is for data preparation, not ongoing model quality monitoring.

質問 # 353
How can companies use large language models (LLMs) securely on Amazon Bedrock?
  • A. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
  • B. Enable AWS Audit Manager for automatic model evaluation jobs.
  • C. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
  • D. Enable Amazon Bedrock automatic model evaluation jobs.
正解：A
解説：
To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents.
Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access": This is the correct answer as it directly addresses both security practices in prompt design and access management.
Option B: "Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage.
Option C: "Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes.
Option D: "Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure.
AWS AI Practitioner Reference:
Secure AI Practices on AWS: AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.

質問 # 354
Which statement presents an advantage of using Retrieval Augmented Generation (RAG) for natural language processing (NLP) tasks?
  • A. RAG is designed to improve the speed of language model training
  • B. RAG is primarily used for speech recognition tasks
  • C. RAG is a technique for data augmentation in computer vision tasks
  • D. RAG can use external knowledge sources to generate more accurate and informative responses
正解：D
解説：
* Retrieval-Augmented Generation (RAG) integrates external knowledge sources (databases, vector stores, document repositories) with LLMs, enabling them to generate contextually accurate and up-to- date responses without retraining.
* B is incorrect: RAG does not speed up training; it improves inference results.
* C is incorrect: speech recognition is not an RAG use case.
* D is incorrect: computer vision augmentation is unrelated to RAG.
# Reference:
AWS Documentation - Knowledge Bases for RAG in Amazon Bedrock

質問 # 355
......
AIF-C01証明書を所有して、自分が有能であることを証明し、特定の分野で優れた実用的な能力を高めることができます。したがって、CertJukenあなたは有能な人々とみなされ、尊敬されます。テストAIF-C01認定に合格すると、目標を実現するのに役立ちます。また、AIF-C01ガイドトレントを購入すると、AIF-C01試験に簡単に合格できます。 AIF-C01試験問題は最も専門的な専門家によって書かれているため、AIF-C01学習教材の品質は素晴らしいです。そして、試験に合格するために、AWS Certified AI Practitioner学習ガイドを常に最新の状態に保ちます。
AIF-C01模擬資料: https://www.certjuken.com/AIF-C01-exam.html
P.S. CertJukenがGoogle Driveで共有している無料かつ新しいAIF-C01ダンプ：https://drive.google.com/open?id=1fY-5wIc8GnkhbfJdTJ_QgCQlnFtBC91U
https://www.getcertkey.com
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