Google Generative-AI-Leader Exam Details & Actual Exam Questions

  • Exam Code/Number: Generative-AI-Leader
  • Exam Name/Title: Google Cloud Certified - Generative AI Leader Exam
  • Certification Provider: Google
  • Corresponding Certification: Google Cloud Certified
  • Exam Questions: 79
  • Updated On: Jul,13 2026
  • Certification Level: Professional

Google Cloud Certified - Generative AI Leader Exam Questions

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Google Generative-AI-Leader Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified - Generative AI Leader Exam
Exam Number:GCP-GAIL
Real Exam Qty:50-60
Related Certifications:Google Cloud Certified - Generative AI Leader
Exam Price:USD 99.00
Available Languages:English
Certificate Validity Period:3 years
Passing Score:Pass / Fail (Approx 70%)
Exam Format:Multiple choice questions with single or multiple correct answers
Exam Duration:90 minutes
Sample Questions:Google Generative-AI-Leader Sample Questions
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 Exam Syllabus Topics:

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


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