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Exam Information
Official specifications published by Google
Exam Format
90 min
50-60
Pass/Fail (approximately 70%)
Foundational
Registration
$99 USD
Kryterion Online Proctored, Kryterion Onsite Test Centers, or online proctoring
English, Japanese
Validity
3 years
Renewal exam within the renewal eligibility period
GCP-GAIL Exam Topics and Domains
GCP-GAIL is organized into 4 weighted domains. Expect to work with RAG APIs, Vertex AI Model Garden, Vertex AI Search, Vertex AI, and more.
1
Fundamentals of Generative AI
30%
Core Generative AI Concepts and Use Cases
Core Gen AI Concept DefinitionsMachine Learning ApproachesMachine Learning Lifecycle StagesChoosing the Appropriate Foundation ModelBusiness Use Cases for Generative AI
- Define core gen AI concepts including AI, NLP, ML, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, and LLMs
- Describe the three machine learning approaches: supervised, unsupervised, and reinforcement learning
- Identify the stages of the machine learning lifecycle and the Google Cloud tools for each stage
- Identify how to choose the appropriate foundation model for a business use case
- Identify business use cases where gen AI can create, summarize, discover, and automate
Data Types in Generative AI and Business Implications
Data Quality and Data AccessibilityStructured vs Unstructured DataLabeled vs Unlabeled Data
- Explain the characteristics and importance of data quality and data accessibility in AI
- Identify the differences between structured and unstructured data with real-world examples
- Identify the differences between labeled and unlabeled data
Core Layers of the Gen AI Landscape
Gen AI Landscape Layers
Identify the five core layers of the gen AI landscape (Infrastructure, Models, Platforms, Agents, Applications) and their business implications
Google's Foundation Models
Google Foundation Model Portfolio
Identify the use cases and strengths of Google's foundation models: Gemini, Gemma, Imagen, and Veo
2
Google Cloud's Generative AI Offerings
35%
Google Cloud's Strengths in Gen AI
Google's AI-First ApproachGoogle's Comprehensive AI EcosystemAI-Optimized InfrastructureData Control and AI Democratization
- Describe how Google's AI-first approach translates into cutting-edge gen AI solutions
- Describe how Google Cloud has an enterprise-ready AI platform
- Recognize the advantages of Google's comprehensive AI ecosystem
- Describe the benefits of Google Cloud's open approach
- Identify essential components of Google Cloud's AI-optimized infrastructure
- Explain how Google Cloud's AI platform provides users with control over their data
- Describe how Google Cloud's AI platform democratizes AI development
Prebuilt Gen AI Offerings for AI-Powered Work
Gemini App and Gemini AdvancedGemini Enterprise (formerly Google Agentspace)Gemini for Google Workspace
- Recognize the functionality, use cases, and business value of the Gemini app and Gemini Advanced
- Recognize the functionality, use cases, and business value of Gemini Enterprise
- Recognize the functionality, use cases, and business value of Gemini for Google Workspace
Gen AI Offerings for Customer Experience
External Search OfferingsCustomer Engagement Suite
- Recognize the functionality, use cases, and business benefits of Google Cloud's external search offerings
- Recognize the functionality, use cases, and business value of Google's Customer Engagement Suite
Empowering Developers to Build with AI
Vertex AI PlatformRAG Offerings on Google CloudVertex AI Agent Builder
- Recognize the functionality, use cases, and business value of Vertex AI Platform
- Recognize the functionality, use cases, and business value of Google Cloud's RAG offerings
- Recognize the functionality, use cases, and business value of using Vertex AI Agent Builder to build custom agents
Tooling for Gen AI Agents
Agent Tools and InteractionsGoogle Cloud Services and Pre-built AI APIsVertex AI Studio vs Google AI Studio
- Identify how agents use tools to interact with the external environment and achieve tasks
- Identify relevant Google Cloud services and pre-built AI APIs for agent tooling
- Determine when to use Vertex AI Studio and Google AI Studio
3
Techniques to Improve Generative AI Model Output
20%
Overcoming Foundation Model Limitations
Common Foundation Model LimitationsGoogle Cloud Recommended Practices to Address LimitationsContinuous Monitoring and Evaluation
- Identify common limitations of foundation models
- Describe Google Cloud-recommended practices to address limitations
- Recognize Google-recommended practices for continuous monitoring and evaluation of gen AI models
Prompt Engineering Techniques
Basic Prompting TechniquesAdvanced Prompting Techniques
- Define prompt engineering and describe its significance in interacting with LLMs
- Identify prompting techniques and use cases (zero-shot, one-shot, few-shot, role prompting, prompt chaining)
- Identify advanced prompting techniques and when to use them (chain-of-thought, ReAct)
Grounding Techniques and Their Use Cases
Grounding ConceptsRetrieval-Augmented Generation (RAG)Sampling Parameters and Settings
- Describe the concept of grounding in LLMs and differentiate between grounding data sources
- Describe how RAG can affect the generated output from gen AI models
- Identify Google Cloud grounding offerings
- Identify how sampling parameters and settings control the behavior of gen AI models
4
Business Strategies for a Successful Gen AI Solution
15%
Implementing a Transformational Gen AI Solution
Types of Gen AI SolutionsChoosing and Integrating Gen AI SolutionsMeasuring Gen AI Impact
- Recognize the different types of gen AI solutions
- Identify the key factors that influence gen AI needs
- Describe how to choose the right gen AI solution for a specific business need
- Identify the steps to integrate gen AI into an organization
- Identify techniques to measure the impact of gen AI initiatives
Secure AI
Security Throughout the ML LifecycleGoogle's Secure AI Framework (SAIF)Google Cloud Security Tools
- Explain security throughout the ML lifecycle
- Identify the purpose and benefits of Google's Secure AI Framework (SAIF)
- Recognize Google Cloud security tools and their purpose
Responsible AI in Business
Responsible AI and TransparencyPrivacy ConsiderationsData Quality, Bias, and FairnessAccountability and Explainability
- Explain the importance of responsible AI and transparency
- Describe privacy considerations including privacy risks, data anonymization, and pseudonymization
- Describe the implications of data quality, bias, and fairness
- Describe the importance of accountability and explainability in AI systems
How do I earn this certification?
Passing GCP-GAIL earns the Google Cloud Certified - Generative AI Leader certification. It sits in the AI / Generative AI / Foundational track.
Current Level Exams
Next Level Options
Alternative Paths
- GCP-CDL - Cloud Digital LeaderComplementary foundational certification covering general cloud concepts and digital transformation beyond AI
- GCP-PMLE - Professional Machine Learning EngineerFor those wanting to deepen technical AI/ML skills after establishing leadership knowledge
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
Use the PlanetCert practice test alongside the study plan below to prepare efficiently for GCP-GAIL.
Who should take this exam?
This exam is typically taken by Business professionals and executives and AI Product Managers.
- Basic understanding of cloud computing concepts
- Familiarity with AI and generative AI terminology
- Business context experience with AI or technology strategy