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

Generative-AI-Leader日本語 Exam Questions
  • Exam Code: Generative-AI-Leader-JPN
  • Exam Name: Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版)
  • Updated: Oct 16, 2026
  • Q & A: 117 Questions and Answers
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Google Generative-AI-Leader日本語 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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:

Question #1

ある企業は生成型AIの導入を検討していますが、ベンダーロックインを懸念しています。テクノロジースタックの柔軟性を維持したいと考えています。Google Cloudのどのような強みが、この懸念を和らげるのでしょうか?

  • A. Google Cloud は独自のテクノロジーを厳格に遵守し、最高レベルのセキュリティとパフォーマンスを保証します。
  • B. Google Cloud の AI ソリューションは簡単に導入できるように事前にパッケージ化されており、カスタマイズや統合の作業は不要です。
  • C. Google Cloud の AI ソリューションは、さまざまなサービスにわたって顧客の選択をサポートするオープンなアプローチを採用しています。
  • D. Google Cloud は自動化に重点を置いており、人間の仕事を AI システムに置き換えることを目指しており、大幅な人員削減につながる可能性があります。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #2

迅速エンジニアリングの定義は何ですか?

  • A. AIモデルに、追加のコンテキストや例なしに、直接的な指示や質問を与える。
  • B. 機械学習を用いてテキストやデータを処理・解釈する人工知能の一分野。
  • C. AIモデルが望ましい応答を生成するように導くための指示を設計する技術と科学。
  • D. モデルの出力を検証可能なソースに接続する機能。
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Correct Answer: C  🗳️

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Question #3

膨大な商品カタログと多数のサポートドキュメントを抱える大手eコマース企業では、顧客がウェブサイト上で必要な情報を見つけるのに苦労しています。これは、サポートコストの高騰とユーザーエクスペリエンスの低下につながっています。同社は、ウェブサイトの検索性を向上させ、サポートコストを削減しつつ顧客満足度を高めるために、Google Cloudソリューションの導入を検討しています。同社はどのGoogle Cloud製品を使用すべきでしょうか?

  • A. Vertex AI Platform
  • B. 頂点AI検索
  • C. Google検索
  • D. Googleショッピング
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #4

Google Cloud の Agent Assist は企業の何の達成に役立ちますか?

  • A. オムニチャネル サポート、ルーティング、CRM システムとの統合を備えたエンタープライズ グレードのコンタクト センター ソリューションを提供するインフラストラクチャ。
  • B. 会話データを分析して、顧客の感情、一般的な議論のトピック、エージェントのパフォーマンスと顧客エクスペリエンスに関する洞察を特定する機能。
  • C. 自動化された顧客サポートのために、決定論的および生成的なチャットボット エージェントを構築および展開する機能。
  • D. ライブカスタマーサービスエージェントとのやり取り中に、リアルタイムのサポートと推奨応答を提供する機能。
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Correct Answer: D  🗳️

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Question #5

ある企業は、複数のステップを含み、様々なシステムへのアクセスを必要とする複雑な業務プロセスを自動化するためにAIを活用したいと考えている。彼らは、トピックの調査、コードのトラブルシューティング、複数のアクションを連鎖させることでシステムにアクセスするなど、複数のステップからなるタスクを処理できるものを必要としている。汎用AIのどの部分が、このような複数のステップからなるタスクを処理できるだろうか?

  • A. エージェント
  • B. インフラ
  • C. モデル
  • D. プラットフォーム
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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