Overview
This lecture covers essential exam questions for the Google Generative AI Leader certification, focusing on correct answers and the reasoning behind them in real-world contexts.
Starting Generative AI Projects
- The first step in a Gen AI project is identifying a specific, high-value business problem to solve.
- Technology adoption should always be driven by clear business needs.
Key AI Model Concepts and Practices
- Hallucination occurs when a model generates incorrect or fictional information.
- Vertex AI is Google Cloud's platform for building, deploying, and managing machine learning models, including Gen AI.
- Prompt design efficiently adjusts a model's tone without retraining.
- Grounding connects a model to internal company data to improve response relevance and accuracy.
- Model Garden in Vertex AI provides a repository for discovering and deploying various foundation models.
Responsible and Secure AI Use
- Privacy and security are core principles of Google's responsible AI practices.
- Auditing training data for bias ensures fairness, especially in hiring-related AI applications.
- Allowing unrestricted user input can lead to prompt injection attacks, posing a security risk.
- Transparency about AI operations and limitations is essential for user trust.
Generative AI Applications and Capabilities
- Content generation, such as personalized email campaigns, is a hallmark Gen AI capability.
- Semantic search and question answering allow AI to understand natural language and retrieve relevant information.
- Retrieval Augmented Generation (RAG) enables access to real-time external knowledge.
- Gemini is Google’s flagship multimodal model for text, images, audio, and video.
Organizational Requirements and Success Metrics
- The availability and quality of relevant data are critical for organizational Gen AI readiness.
- Moving an AI application to production requires new focus on scalability, monitoring, and cost management.
- AI project success is best measured by improved business outcomes like reduced workload and higher customer satisfaction.
Human and Technical Workflow
- Fine-tuning changes internal model weights and is resource-intensive; prompt tuning does not.
- A human-in-the-loop reviews and corrects AI outputs to ensure quality.
- Generative AI Studio enables non-technical users to interact with foundation models via a no-code interface.
Key Terms & Definitions
- Hallucination — When a model generates factually incorrect or invented information.
- Grounding — Connecting a model to specific real-time business data for accuracy.
- Model Garden — A repository for accessing and deploying various foundation models in Vertex AI.
- Prompt injection — A security attack where user input causes unintended model behavior.
- Retrieval Augmented Generation (RAG) — Enhancing models by retrieving information from external sources before generating answers.
- Human-in-the-loop — Human oversight to validate and correct AI outputs.
Action Items / Next Steps
- Review these key concepts before the certification exam.
- Practice with official practice tests to reinforce your understanding.
- Explore Google Cloud documentation on Vertex AI, Model Garden, and Responsible AI principles.