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Exam Code: Generative-AI-Leader
Exam Name: Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版)
Certification Provider: Google
Corresponding Certification: Google Cloud Certified

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Generative-AI-Leader日本語

Updated: Oct 21, 2026

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Google Generative-AI-Leader日本語 Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Generative AI Leader Certification Exam
Exam Number:Generative-AI-Leader
Real Exam Qty:50-60
Exam Duration:90 minutes
Exam Price:USD 99 (plus tax where applicable)
Exam Format:Multiple choice
Available Languages:Japanese, Spanish, Portuguese, English
Passing Score:Not publicly disclosed
Certificate Validity Period:3 years
Recommended Training:Generative AI Leader Training Course
Generative AI Leader Study Guide
Exam Registration:Google Cloud Certification Registration
Sample Questions:Google Generative-AI-Leader日本語 Sample Questions
Exam Way:Online-proctored or onsite-proctored
Pre Condition:No prerequisites required; open to all roles and backgrounds
Official Syllabus URL:https://cloud.google.com/learn/certification/generative-ai-leader

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
  • 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.
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
  • 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.

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

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

Question #1

ある企業が生成AI戦略を策定中です。成功の可能性を高めるため、Googleが推奨するプラクティスに従おうと考えています。どの戦略を採用すべきでしょうか？

  • A. トップダウン戦略
  • B. 迅速な実装戦略
  • C. 多方向戦略
  • D. ボトムアップ戦略
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #2

ある市場調査会社は、Google Cloud の既成の生成型 AI を活用して、長文の市場レポートや調査論文から情報を抽出・統合するプロセスを効率化したいと考えています。彼らの目標は、効率性を向上させ、顧客に迅速なインサイトを提供することです。この組織はどのような対応を取るべきでしょうか？

  • A. Geminiアプリを使用して、市場動向の概要を把握してください。
  • B. Google WorkspaceとGeminiを使用して、チームメンバーが要約やレポートを作成するのを支援します。
  • C. 文書と対話して情報を抽出するためのカスタム対話型エージェントを構築する。
  • D. NotebookLMを使用してファイルをアップロードし、分析結果を抽出します。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #3

カスタマーサポートチームは、製品に関する質問に回答するために生成型AIエージェントを使用しています。しかし、このAIは、トレーニングデータに含まれていない社内記事に記載されている新機能やトラブルシューティングに関する質問には回答できません。彼らは、回答の質を向上させるために、どのような費用対効果の高い手法を用いるべきでしょうか？

  • A. 役割に基づくプロンプト
  • B. 検索拡張生成(RAG)
  • C. 新しいドキュメントを使用して基礎モデルを微調整する
  • D. 例題付きの基本的なプロンプト
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #4

あるプロジェクトチームは、長文の文書を処理・要約する必要のある生成型AIアプリケーションを開発しています。彼らは、基盤となる言語モデルの制約について検討しています。このアプリケーションにおけるトークン数に関して、重要な考慮事項は何でしょうか？

  • A. トークン数が少ないほど、一般的にモデルからより創造的で多様な出力が得られます。
  • B. トークン数は、主にAIモデルの安全設定に影響を与え、トークン数が多いほど潜在的に有害なコンテンツをフィルタリングします。
  • C. このモデルにはトークン制限があります。制限を超えると、プロンプトに含まれるドキュメント全体を処理できなくなる可能性があります。
  • D. トークン数を増やすと、AI モデルに必要な処理能力が低下します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #5

迅速エンジニアリングの定義は何ですか？

  • A. モデルの出力を検証可能なソースに接続する機能。
  • B. 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。
  • 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. 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。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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