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

Generative-AI-Leader日本語 real exams

Exam Code: Generative-AI-Leader-JPN

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

Updated: Oct 28, 2026

Q & A: 117 Questions and Answers

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About Google Generative-AI-Leader日本語 Exam

Google Generative-AI-Leader日本語 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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.

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
教師なし機械学習の例は何ですか?

A. ラベル付きカテゴリを使用して製品画像を認識するようにシステムをトレーニングします。
B. 過去の更新ステータス データに基づいてサブスクリプションの更新を予測します。
C. 過去の販売およびマーケティング費用を使用して売上高を予測します。
D. 顧客の購入パターンを分析して自然なグループ分けを識別します。


Question #2
地理的に分散したチームを抱える大規模な多国籍企業は、知識のサイロ化と重要な社内情報への一貫性のないアクセスに悩まされています。このシナリオにおいて、Google Agentspace を利用することによる主なビジネス上のメリットは何でしょうか?

A. AI を使用した従業員の業績レビューの自動化。
B. 社内システム間でのシームレスな知識共有とコラボレーション。
C. オフィス全体の IT インフラストラクチャ管理が改善されました。
D. 内部通信のデータ暗号化とコンプライアンスが強化されました。


Question #3
マネージャーは、AIモデルに質の高いデータのみが使用されるようにしたいと考えています。どのシナリオが、不公平で偏った結果につながる可能性が最も高いでしょうか?

A. 金融機関が、不完全な取引データを使用して不正検出モデルを構築します。このデータは、取引の大部分に関与する加盟店に関する詳細情報が欠落しています。
B. マーケティングチームが、ボットトラフィックを含むウェブサイトのクリックストリームデータに基づいて、パーソナライズされたレコメンデーションエンジンを作成します。
C. あるテクノロジー企業が、主に特定の人口統計グループの個人の画像を使用して顔認識システムを訓練し、他のグループからの表現は限定的である。
D. ある企業の顧客サービス自然言語処理モデルは、古いウェブサイトのバージョンからの誤ってエンコードされたテキストを含むデータセットでトレーニングされています。


Question #4
Googleが推奨する実践方法によると、戦略的思考プロセスを強化するために、生成型AIはどのような場合に活用すべきでしょうか?

A. 医療診断や法律相談など、機密情報に関わる業務や人間の監視が必要な業務の場合
B. 財務報告や会計など、高いレベルの正確性と精密さが求められるタスクの場合。
C. 顧客サービスに関する問い合わせに対するテキスト応答の生成など、反復的でルールに基づいたタスクの場合。
D. データ分析やコンテンツ作成など、批判的思考、創造性、戦略的意思決定を伴うタスクの場合。


Question #5
サポートチームは、顧客サポートチケットをさまざまなカテゴリ(例:請求に関する問題、技術サポート、機能リクエスト)に分類する生成型AIモデルを構築しています。彼らは、過去のチケットとその正しいカテゴリのデータセットを持っており、このタスクのためにAIモデルをトレーニングする必要があります。どのようなプロンプト手法を使用すべきでしょうか?

A. ワンショットプロンプト
B. 少ショットプロンプト
C. ゼロショットプロンプト
D. 役割の促進


Solutions:

Question #1
Correct Answer: D
Question #2
Correct Answer: B
Question #3
Correct Answer: C
Question #4
Correct Answer: D
Question #5
Correct Answer: B

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