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Reading notes is one thing; sitting a timed exam is another. The desktop test engine from Exams4sures recreates the pressure of the real Generative-AI-Leader日本語 exam with two practice modes, letting you rehearse the Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版) offline until it feels routine in 2026.

Google Generative-AI-Leader日本語 Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified - Generative AI Leader Exam
Exam Number:GCP-GAIL
Exam Price:USD 99.00
Passing Score:Pass / Fail (Approx 70%)
Real Exam Qty:50-60
Certificate Validity Period:3 years
Exam Duration:90 minutes
Available Languages:English
Related Certifications:Google Cloud Certified - Generative AI Leader
Exam Format:Multiple choice questions with single or multiple correct answers
Sample Questions: DOWNLOAD DEMO
Exam Way:Remote as well as onsite
Pre Condition:This certification is for anyone in any job role, with or without hands-on technical experience.
Official Syllabus URL:https://cloud.google.com/learn/certification/generative-ai-leader

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

SectionWeightObjectives
Topic 1: Business strategies for a successful gen AI solution15%- Describe best practices for a successful gen AI project.
  • 1. Building a business case
  • 2. Evaluating AI solutions
  • 3. Choosing the right model
- Describe change management best practices and their importance.
  • 1. Enabling AI adoption
  • 2. Creating a culture of innovation
- Describe Google's approach to responsible AI and its importance.
  • 1. Google's AI principles
  • 2. Responsible AI best practices
Topic 2: Fundamentals of generative AI30%- Describe how various data types are used in gen AI and the business implications.
  • 1. Identifying the differences between structured and unstructured data, and identifying real world examples of each type
  • 2. Identifying the differences between labeled and unlabeled data
  • 3. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format)
- Describe core generative AI (gen AI) concepts and use cases.
  • 1. Identifying the stages of the machine learning lifecycle (e.g., data ingestion, data preparation, model training, model deployment, model management) and the Google Cloud tools for each stage
  • 2. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement)
  • 3. Defining core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models)
  • 4. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost)
- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Agents
  • 2. Infrastructure
  • 3. Applications
  • 4. Models
  • 5. Platforms
Topic 3: Google Cloud's generative AI offerings35%- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Google Workspace
  • 2. Vertex AI
  • 3. Gemini for Google Cloud
  • 4. Vertex AI Studio
  • 5. Model Garden
- Identify the use cases and strengths of Google's foundation models.
  • 1. Gemma
  • 2. Imagen
  • 3. Veo
  • 4. Gemini
Topic 4: Techniques to improve gen AI model output20%- Describe the process of fine-tuning gen AI models.
  • 1. Reinforcement learning from human feedback (RLHF)
  • 2. Supervised tuning
- Describe how grounding can be used to improve model output.
  • 1. Grounding with Google Search
  • 2. Grounding with enterprise data
- Describe prompt engineering techniques and their purpose.
  • 1. Zero-shot
  • 2. Few-shot
  • 3. Chain of thought
  • 4. One-shot

Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版) FAQs: What Candidates Ask Before Exam Day

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The Generative-AI-Leader日本語 exam contains 50-60 questions and gives you 90 minutes to complete them. That works out to a brisk pace, so you cannot afford to stall on a single tricky item — flag it, move on, and circle back if time allows. Before exam day, run at least two timed mock sessions with the Exams4sures desktop or online test engine; practicing against a real clock turns the official time limit into a comfortable rhythm instead of a source of panic.

The Generative-AI-Leader日本語 exam (full name: Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版)) is a Google certification exam that leads to the Google Cloud Certified credential, positioned at the Professional level. It validates the skills Google expects from professionals working with its technologies, and passing it is the key step toward getting certified. It also sits within a broader certification path that includes Google Cloud Certified - Generative AI Leader.

The Google Cloud Certified - Generative AI Leader Exam (Generative-AI-Leader日本語版) exam is organized into 4 domains. The main areas include Google Cloud's generative AI offerings (35%), Techniques to improve gen AI model output (20%), and Fundamentals of generative AI (30%). Scroll up to the exam syllabus section on this page for the complete topic-by-topic outline.

This certification is for anyone in any job role, with or without hands-on technical experience.

Eligibility requirements can change, so before you register, confirm the latest criteria on the official Google exam page.

To pass the Generative-AI-Leader日本語 exam you need a score of Pass / Fail (Approx 70%), and the official registration fee is USD 99.00. Keep in mind that a failed attempt comes at full price: retaking the exam means paying the entire fee again. A smart way to protect that investment is to self-test first — work through the 117 practice questions at Exams4sures until your timed scores sit comfortably above the passing line before you book a seat.

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

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. 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。
  • 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. 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。
  • 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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ソフトウェア開発者は、チャットボットアプリケーションの迅速なプロトタイピングのために、ローカルマシンで微調整可能な、高効率でオープンソースの大規模言語モデルを必要としています。自然言語の理解と生成において優れたパフォーマンスを発揮する一方で、限られたハードウェアでも実行できるほど軽量なモデルが必要です。Googleが開発したどのモデルファミリーを使用すべきでしょうか?

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

Correct Answer: C  🗳️

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ある企業が、生成AIを活用して、自社製品やサービスに関する顧客の質問に答えるチャットボットを開発したいと考えています。チャットボットが使用する情報は、企業の公式ドキュメントに記載されている情報のみであることを保証する必要があります。企業は何をすべきでしょうか?

  • A. 接地を使用します。
  • B. ロールプロンプトを使用します。
  • C. プロンプトチェーンを使用します。
  • D. 温度パラメータを調整します。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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生成 AI 環境におけるインフラストラクチャ層のコアハードウェアコンポーネントは何ですか?

  • A. 事前学習済みモデル
  • B. AIモデルを構築するためのツールとサービス
  • C. TPUとGPU
  • D. ユーザーインターフェース
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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ある全国銀行は、複数のチャネルを通じた顧客からの問い合わせに圧倒されており、シームレスで一貫したサポートの提供、カスタマーサポートエージェントの能力強化、そしてサービス品質の向上を実現するために、AIを活用したソリューションを必要としています。この銀行はどのGoogle Cloudプロダクトを活用すべきでしょうか?

  • A. Google コンタクト センター サービス
  • B. Vertex AI 検索
  • C. Google Cloud 向け Gemini
  • D. Google Workspace 向け Gemini
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for Exams4sures members. You can sign-up / login (it's free).

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