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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 20, 2026

No. of Questions: 117 Questions & Answers with Testing Engine

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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
  • 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 3
  • 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 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

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
Exam Format:Multiple choice
Exam Duration:90 minutes
Exam Price:USD 99 (plus tax where applicable)
Certificate Validity Period:3 years
Passing Score:Not publicly disclosed
Real Exam Qty:50-60
Available Languages:Spanish, Portuguese, Japanese, English
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 Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版) Sample Questions:

Question #1

マーケティングチームは、ソーシャルメディアや広告キャンペーンを作成するために基盤モデルを使用したいと考えています。テキストから記事や画像を作成したいと考えています。AIに関する深い専門知識がないため、汎用性の高いソリューションが必要です。
どの Google 基盤モデルを使用すればよいでしょうか?

  • A. ジェミニ
  • B. なるほど
  • C. ジェマ
  • D. 画像
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #2

拡散モデルは何をしますか?

  • A. ビジネス プロセスとリソースの割り当てを最適化します。
  • B. 構造化データの保存と管理を容易にします。
  • C. ノイズを構造化データに精製して、高品質のコンテンツを生成します。
  • D. データを分析し、将来の傾向とパターンを予測します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #3

アニメーションスタジオは、シーンやキャラクターの行動を記述した文章に基づいて、短いアニメーション作品を迅速に制作する必要があります。彼らはアニメーションのストーリーボードをプレビューし、ストーリーや流れについて迅速なフィードバックを得たいと考えています。なぜ彼らはこの作業にVeoを使うべきなのでしょうか？

  • A. Veoは、高品質で自然な音声を生成するように最適化されています。
  • B. Veoは軽量モデルで、特殊なAIアプリケーション向けにカスタマイズ可能なソリューションを提供します。
  • C. Veoは、アプリケーション用のコードを処理および生成できる強力なマルチモーダルモデルです。
  • D. Veoは、テキストや静止画から動画を生成することに最適化されています。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

ある企業が様々な生成型AI(gen AI)プラットフォームを評価しており、gen AIモデルの開発と展開をサポートする上でインフラストラクチャ層が果たす役割を理解したいと考えています。gen AI環境におけるインフラストラクチャ層の役割とは何でしょうか？

  • A. 汎用AIアプリケーションの構築と展開のためのツールとリソースを提供する。
  • B. AIモデルの実行とトレーニング、およびトレーニングデータの保存に必要なコンピューティングリソースを提供します。
  • C. gen AI モデルと対話するためのユーザーフレンドリーなインターフェースを提供する。
  • D. 事前学習済みの汎用AIモデルへのアクセスを提供する。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

研修・開発チームは、会社のブランディングと主要メッセージにマッチしたカスタムアバターとナレーション付きの新入社員研修ビデオを迅速に制作したいと考えています。制作費は一切支給されていません。どうすればよいでしょうか？

  • A. Imagen を使用してビデオ フレームを生成します。
  • B. Vertex AI を使用してモデルをトレーニングし、ビデオを生成します。
  • C. Google Vids を使用してビデオを作成します。
  • D. Gemini アプリにビデオの作成を促します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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