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Course Outline
Introduction
- Value and target audience for the Generative AI Leader certification
- Assessment structure, competency domains, and preparation requirements
Generative AI Fundamentals (~30%)
- Core concepts and applications in generative AI, including AI, ML, LLMs, foundation models, multimodal and diffusion architectures, and prompt engineering
- Machine learning methodologies (supervised, unsupervised, reinforcement learning) and the associated lifecycle
- Criteria for selecting foundation models, covering modality, context window, cost, performance, and customization capabilities
- Data classification and quality standards in generative AI, distinguishing between structured/unstructured and labeled/unlabeled datasets
- Generative AI architecture layers and Google’s foundation models, such as Gemini, Gemma, Imagen, and Veo
Google Cloud Generative AI Capabilities (~35%)
- Competitive advantages and AI-optimized infrastructure, including TPUs, GPUs, and hypercomputer systems
- Pre-configured solutions: Gemini applications, Gemini Advanced, Gemini for Google Workspace, and Gemini Enterprise
- Customer engagement tools: Conversational Agents, Agent Assist, and Conversational Insights
- Developer resources: Vertex AI and Agent Platform, Model Garden, and Retrieval-Augmented Generation (RAG) solutions
- Tooling for generative AI agents, including extensions, functions, data stores, and supporting Google Cloud services
Techniques for Enhancing Generative AI Model Output (~20%)
- Mitigating foundation model constraints, such as knowledge cutoffs, bias, hallucinations, and edge cases
- Prompt engineering methods, including zero-shot, one-shot, few-shot, role-based, prompt chaining, chain-of-thought, and ReAct techniques
- Grounding strategies and Retrieval-Augmented Generation (RAG) implementation
- Configuration of sampling parameters to manage output, including temperature, top-p, token limits, and safety controls
Strategic Frameworks for Generative AI Success (~15%)
- Implementation procedures and methodologies for solution selection
- Secure AI practices and the Google Secure AI Framework (SAIF)
- Responsible AI principles, encompassing privacy, bias mitigation, fairness, accountability, and explainability
Assessment Preparation
- Sample questions and domain-specific review sessions
- Comprehensive mock exam and detailed answer analysis
- Structured study plan and exam-day operational strategy
Summary and Follow-Up Actions
Requirements
Prerequisites
- No specific technical prerequisites are required
- General familiarity with business technology is beneficial
Intended Audience
- Leaders, managers, and strategic decision-makers
- Business professionals in any role integrating generative AI
- Individuals preparing for the Google Cloud Generative AI Leader certification
14 Hours
Testimonials (1)
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