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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- Total Questions: 117
- Updated on: Oct 21, 2026
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- Total Questions: 117
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Google Generative-AI-Leader日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Business Applications and Adoption Strategy | - Identifying business use cases for generative AI - AI-driven transformation and workflow integration - Measuring ROI and value of generative AI initiatives |
| Fundamentals of Generative AI | - Key use cases and limitations of generative AI - Core concepts of generative AI and large language models - Difference between traditional AI, machine learning, and generative AI |
| Responsible AI and Governance | - Data privacy and security in generative AI systems - AI safety, bias, and fairness considerations - Responsible AI principles and compliance |
| Google Cloud Generative AI Products and Tools | - AI APIs and model deployment options on Google Cloud - Vertex AI and Gemini models overview - Prompt design and prompt engineering tools |
Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版) Exam: Questions Worth Asking
The Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版) exam contains 50-60 to complete within 90 minutes. Timed simulation in the TestKingFree Desktop Test Engine teaches that rhythm before exam day does.
Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows with customizable simulation and timed modes that works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 117 practice questions are identical across versions. Every purchase includes 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.
The official Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版) outline defines 4 domains. The leading three are Responsible AI and Governance, Business Applications and Adoption Strategy, and Fundamentals of Generative AI. The full list is in the topics section above, and TestKingFree's 117 practice questions cover each one.
Scoring and fees are published by Google and may change; verify the current figures on the official site.
The Generative-AI-Leader日本語 exam is a Google certification exam validating the Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版) syllabus shown above. It belongs to these credential paths: Google Cloud Digital Leader, Google Cloud Professional Machine Learning Engineer. Candidates in 2026 keep up with it using TestKingFree's 117 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
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No strict prerequisites; basic understanding of cloud computing and AI concepts recommended Since requirements change over time, confirm the latest on the official Google exam page before scheduling.
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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AIエージェントを使っていくつかのタスクを自動化したいと考えています。AIエージェントの様々な機能を全員に理解してもらいたいと考えています。ジェネレーションAIの文脈におけるAIエージェントの機能は何でしょうか？
- A. 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します。
- B. 状況を分析し、複数のツールを使用して、継続的な人間による入力を必要とせずに情報に基づいた意思決定を行います。
- C. gen AI モデルのトレーニングと実行に必要な計算リソースを提供します。
- D. gen AI モデルのトレーニングと実行に使用される大規模なデータセットを保存および管理します。
Correct Answer: B 🗳️
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ある企業が、顧客向けマーケティングコンテンツのパーソナライズに生成型AIモデルを使用しています。モデルの学習に使用される顧客データには、顧客名と購入履歴が含まれます。同社は、パーソナライズのメリットと顧客のプライバシー保護の必要性とのバランスを取りたいと考えています。このデータを処理する際に、どのような手法を用いるべきでしょうか？
- A. 仮名化を使用して、直接的な識別子を人工的な識別子に置き換える。
- B. AIモデルの出力が差別的でないことを確認するために、定期的に監査を実施する。
- C. 顧客データを安全で暗号化されたサーバーに保存する。
- D. AIモデルで顧客データを使用することについて、顧客から明示的な同意を得る。
Correct Answer: A 🗳️
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あるチームが、生成型AI(gen AI)の環境における様々なレイヤーと、それらのレイヤー内で特定のツールやテクノロジーがどのような位置づけになるかについて議論しています。彼らは、Gemini Enterprise Agent Platformとデータ管理ツールが、この環境全体の中でどのような役割を果たすのかを明確にしたいと考えています。Gemini Enterprise Agent Platformとデータ管理ツールは、生成型AIの環境のどのレイヤーに位置するのでしょうか？
- A. エージェント
- B. インフラ
- C. プラットフォーム
- D. モデル
Correct Answer: C 🗳️
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どのような状況で、言語モデルを第一者情報に基づいて構築することが最も有益となるでしょうか？
- A. 顧客が企業のチャットボットに、最近の購入履歴に関する具体的な詳細を問い合わせます。
- B. ユーザーが汎用AIアシスタントに一般的な科学用語の定義を尋ねます。
- C. アナリストは、言語モデルを使用して、世界中のさまざまな情報源からのニュース記事を要約したいと考えています。
- D. マーケティングチームは、自社の業界を取り巻く世論を理解するために言語モデルを使用したいと考えています。
Correct Answer: A 🗳️
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ある企業の開発チームは、Google Cloud を利用した生成型 AI ソリューションの構築に意欲的ですが、AI 開発の経験が限られています。そのため、生成型 AI ソリューションを迅速にリリースする必要があります。この目標達成に役立つ Google Cloud のメリットは何でしょうか。
- A. Google Cloud の共同 AI コミュニティとサポート フォーラムは、開発者と AI エキスパートを結び付けます。
- B. Google Cloud の事前トレーニング済みモデルと、ローコードおよびノーコードの AI ツールとサービス。
- C. 開発者を支援する Google Cloud の包括的なトレーニング マテリアルとチュートリアル。
- D. Google Cloud は継続的な改善に重点を置いており、最新の AI ツール、機能、ベスト プラクティスにアクセスできます。
Correct Answer: B 🗳️
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