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

Generative-AI-Leader日本語 Exam Simulator
  • 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
  • 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
  • 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
  • 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.

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

ある住宅ローン会社は、ローン申請の初期審査を自動化するために、生成AIシステムを導入しています。複数の申請者が予期せず却下され、顧客からの苦情や潜在的なバイアスへの懸念が生じています。責任ある公正な融資慣行を確保する必要があります。AIシステムのどの側面を優先すべきでしょうか?

  • A. 時間の経過とともに精度を向上させるために、より多くの財務データを使用して AI モデルを定期的に更新します。
  • B. AI の意思決定が説明可能であることを確認して、意思決定の理由を理解し、説明責任を確立します。
  • C. 大量のローン申請を処理するために、AI システムがローン申請を処理する速度を向上させます。
  • D. 申請者の財務情報を不正アクセスから保護するために、より厳格なデータセキュリティ対策を実施します。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

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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Question #3

教師あり機械学習の例を挙げてください。

  • A. ウェブサイトのクリックを分析して、ユーザー行動のクラスターを見つけます。
  • B. 購入履歴を利用して、未知の顧客セグメントを特定する。
  • C. 過去のラベル付きサポートチケットを使用して解決時間を予測するモデルを構築する
  • D. 顧客レビューを分析して、繰り返し出現するトピックを自動的に特定します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #4

ある製薬会社の研究開発部門は、潜在的な創薬ターゲットを特定するために、新しい科学論文を手作業でレビューすることに多大な時間を費やしています。高度なコーディングの専門知識を必要とせずに、これらの論文に関する疑問に答え、研究者に要約された洞察を提供できるソリューションが必要です。組織は何をすべきでしょうか?

  • A. Vertex AI Agent Builder を使用してカスタム AI エージェントを作成します。
  • B. Vertex AI AutoML を使用して、論文を事前定義された研究分野に分類するモデルをトレーニングします。
  • C. Gemini for Google Workspace を使用して、共同ドキュメントレビューを容易にします。
  • D. Vertex AI Search を使用して論文のインデックスを作成し、キーワードベースの検索を有効にします。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

開発チームは顧客向けアプリケーション用の生成AIモデルを構成しており、生成されるコンテンツが適切かつ無害であることを確認したいと考えています。生成AIモデルにおける安全性設定パラメータの主な機能は何ですか?

  • A. 単語選択の多様性を調整することで、モデルの出力の創造性とランダム性を制御します。
  • B. 簡潔な応答を保証することで、モデルが生成するテキストの最大長を制限します。
  • C. 必要なフィルタリング レベルに基づいて、モデルの出力から潜在的に有害または不適切なコンテンツをフィルタリングします。
  • D. 入力と出力の複雑さと長さに影響を与えることで、モデルが一度に処理できるトークンの数を決定します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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