GOOGLE CLOUDGoogle Cloud Generative AI Leader
The Google Cloud Generative AI Leader certification validates your ability to articulate the business value of generative AI and lead AI-driven transformation initiatives. Designed for leaders and decision-makers, it demonstrates that you can identify high-impact use cases, evaluate AI solutions, and drive responsible adoption across your organization. Earning this credential signals that you can bridge AI strategy and business outcomes.
418 practice questions · Updated 2026-07-30
4Domains
15Objectives
106Concepts
418Questions
GENERATIVE-AI-LEADER Curriculum
Every domain, objective, and concept the GENERATIVE-AI-LEADER exam measures.
- Core Gen AI Terminology
- Machine Learning Approaches
- Machine Learning Lifecycle Stages
- Foundation Model Selection Criteria
- Gen AI Business Use Cases
- Data Types in Gen AI
- Data Quality and Accessibility
- Structured vs. Unstructured Data
- Labeled vs. Unlabeled Data
- Data quality characteristics
- Data accessibility factors
- Structured vs unstructured data
- Labeled vs unlabeled data
- Infrastructure layer
- Model layer
- Platforms layer
- Agents layer
- Applications layer
- Gemini use cases
- Gemini strengths
- Gemma use cases
- Gemma strengths
- Imagen use cases
- Imagen strengths
- Veo use cases
- Veo strengths
- Google's AI-first approach and innovation
- Enterprise-ready AI platform attributes
- Comprehensive AI ecosystem integration
- Open approach benefits
- AI-optimized infrastructure components
- Data control and governance
- Democratization of AI development
- Gemini app and Gemini Advanced functionality
- Gemini app and Gemini Advanced use cases
- Gems in Gemini Advanced
- Business value of Gemini app and Gemini Advanced
- Gemini Enterprise functionality
- Gemini Enterprise use cases
- Business value of Gemini Enterprise
- Gemini for Google Workspace functionality
- Gemini for Google Workspace use cases
- Business value of Gemini for Google Workspace
- External Search Offerings Overview
- Use Cases for External Search
- Business Benefits of External Search
- Customer Engagement Suite Overview
- Conversational Agents Functionality and Use Cases
- Agent Assist Functionality and Use Cases
- Conversational Insights Functionality and Use Cases
- Contact Center as a Service (CCaaS) Functionality and Use Cases
- Business Value of Customer Engagement Suite
- Agent Platform overview
- Model Garden functionality
- Agent Search use cases
- Agent Platform AutoML
- Prebuilt RAG with Agent Search
- RAG APIs
- Building custom agents with Agent Platform
- Agent-tool interaction model
- Types of agent tools
- Google Cloud services for agent tooling
- Pre-built AI APIs for agent tooling
- Google Cloud API Library
- Agent Studio vs. Google AI Studio
- Foundation model limitations
- Google-recommended mitigation practices
- Continuous monitoring and evaluation practices
- Definition of prompt engineering
- Significance of prompt engineering for LLMs
- Zero-shot prompting
- One-shot prompting
- Few-shot prompting
- Role prompting
- Prompt chaining
- Chain-of-thought prompting
- ReAct prompting
- Grounding in LLMs
- Types of Grounding Data
- Impact of RAG on Model Output
- Pre-built RAG with Agent Search
- RAG APIs
- Grounding with Google Search
- Sampling Parameters Overview
- Temperature and Top-P
- Token Count and Output Length
- Safety Settings
- Types of Gen AI Solutions
- Factors Influencing Gen AI Needs
- Selecting the Right Gen AI Solution
- Integration Steps for Gen AI
- Measuring Gen AI Impact
- Security throughout the ML lifecycle
- Purpose and benefits of Google's Secure AI Framework (SAIF)
- Secure-by-design infrastructure
- Identity and Access Management (IAM) for AI
- Security Command Center for AI
- Workload monitoring tools for AI
- Importance of responsible AI
- Transparency in AI
- Privacy risks in AI
- Data anonymization and pseudonymization
- Data quality implications
- Bias and fairness in AI
- Accountability in AI systems
- Explainability in AI systems
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