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Google Generative-AI-Leader日本語 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.
Topic 2
  • Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
Topic 3
  • Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.
Topic 4
  • Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.

Reference: https://cloud.google.com/developers/generative-ai-leader

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Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版) Sample Questions:

ある企業が、生成型AIを搭載した顧客サポートチャットボットを開発しています。同社は、チャットボットが、モデルの元のトレーニングデータに含まれていない、最近更新された製品情報に関する質問も含め、幅広い顧客からの質問に正確に回答できるようにしたいと考えています。このチャットボットに検索拡張生成(RAG)を実装する主な利点は何でしょうか?

  • A. RAGは主にチャットボットがより創造的で魅力的な会話応答を生成するのに役立ちます。
  • B. RAGは、チャットボットが外部の最新の知識ソースにアクセスして利用し、より正確で関連性の高い回答を提供できるようにします。
  • C. RAGは、生成AIモデルを実行するために必要な計算リソースを大幅に削減します。
  • D. RAGは、チャットボットが顧客とのやり取りに基づいて、基盤となる言語モデルをリアルタイムで微調整することを可能にします。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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AIエージェントの「思考プロセス」を構成する、観察、解釈、計画、行動という連続的なサイクルとは何でしょうか?

  • A. プラットフォーム統合
  • B. ユーザーインターフェース
  • C. モデルトレーニング
  • D. 推論ループ
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Vertex AI Search によって企業は何ができるようになりますか?

  • A. 世界中のユーザーの検索パターンと傾向に基づいて、最も人気があり頻繁にアクセスされるコンテンツを表示します。
  • B. LLM 応答をファーストパーティ データ、サードパーティ データ、および Google のナレッジ グラフに基づいて構築します。
  • C. 公開 Web 全体から情報をインデックスして取得し、公開されているデータの包括的なビューを提供します。
  • D. 多数のオンライン小売業者の製品を比較し、ユーザーがインターネット上で最良の取引と製品オプションを見つけられるようにします。
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Correct Answer: B  🗳️

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ある企業が、生成AIを活用して、自社製品やサービスに関する顧客の質問に答えるチャットボットを開発したいと考えています。チャットボットが使用する情報は、企業の公式ドキュメントに記載されている情報のみであることを保証する必要があります。企業は何をすべきでしょうか?

  • A. プロンプトチェーンを使用します。
  • B. 温度パラメータを調整します。
  • C. 接地を使用します。
  • D. ロールプロンプトを使用します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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ある企業の大規模学習モデル(LLM)は、知識の限界によって幻覚を生み出しています。検索拡張生成(RAG)は、この限界をどのように克服するのでしょうか?

  • A. RAG により、LLM は知識ソースから関連性のある最新の情報を取得できるようになります。
  • B. RAG は、顧客に情報を提示する前に、人間による監視を行って正確性を確保します。
  • C. RAG は LLM の創造的なライティング能力を強化し、より魅力的で有益な応答を生み出します。
  • D. RAG は、特定の顧客クエリ パターンに基づいて LLM を微調整し、応答生成の速度と効率を向上させます。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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