Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Overview
- Value proposition and target audience for the Generative AI Leader certification
- Exam structure, domain weightings, and preparation guidelines
Generative AI Fundamentals (~30%)
- Essential concepts and applications, including artificial intelligence, machine learning, large language models, foundation models, multimodal and diffusion architectures, and prompt engineering
- Machine learning methodologies (supervised, unsupervised, reinforcement) and the machine learning lifecycle
- Criteria for selecting foundation models, such as modality, context window size, cost efficiency, performance metrics, and customization capabilities
- Data typologies and quality considerations in generative AI, distinguishing between structured versus unstructured and labeled versus unlabeled data
- The layers of the generative AI ecosystem and Google’s foundation model portfolio (Gemini, Gemma, Imagen, Veo)
Google Cloud Generative AI Services (~35%)
- Competitive advantages of Google Cloud’s generative AI offerings and AI-optimized infrastructure, including TPUs, GPUs, and hypercomputer technology
- Pre-built solutions: Gemini app and Advanced tier, Gemini for Google Workspace, and Gemini Enterprise
- Customer experience enhancements via the Customer Engagement Suite (Conversational Agents, Agent Assist, Conversational Insights)
- Developer tools and resources: Vertex AI / Agent Platform, Model Garden, and Retrieval-Augmented Generation (RAG) solutions
- Generative AI agent capabilities, including extensions, functions, data stores, and associated Google Cloud services
Optimizing Generative AI Model Outputs (~20%)
- Mitigating foundation model constraints, such as knowledge cutoff dates, bias, hallucinations, and edge cases
- Prompt engineering strategies: zero-shot, one-shot, few-shot, role-based, prompt chaining, chain-of-thought, and ReAct patterns
- Implementation of grounding techniques and Retrieval-Augmented Generation (RAG)
- Configuration of sampling parameters to regulate output, including temperature, top-p, token limits, and safety settings
Business Strategies for Effective Generative AI Deployment (~15%)
- Steps for implementation and methodologies for solution selection
- Secure AI practices and Google’s Secure AI Framework (SAIF)
- Responsible AI principles: privacy protection, bias mitigation and fairness, accountability, and explainability
Exam Preparation
- Sample questions and domain-specific review
- Comprehensive mock examination with detailed answer analysis
- Study roadmap and strategies for exam day
Conclusion and Next Steps
Requirements
Requirements
- Prerequisite technical skills are not mandated.
- A foundational understanding of business technology is advantageous.
Target Audience
- Executive leadership, management personnel, and decision-makers.
- Business professionals across all functional roles who implement generative AI solutions for government agencies.
- Candidates preparing to obtain the Google Cloud Generative AI Leader certification.
14 Hours
Testimonials (1)
Flow , vibe and topic on presentation
