Core concepts: LLMs, foundation models, prompting, RAG, fine-tuning, AI lifecycle
Domain Weight
Fundamentals of gen AI accounts for 27% of the GCP-GAIL exam.
What This Domain Covers
Core concepts: LLMs, foundation models, prompting, RAG, fine-tuning, AI lifecycle
Key Topics to Master
- What generative AI is and how it differs from traditional ML
- Large language models (LLMs): training, inference, tokens, and context windows
- Foundation models and their capabilities (text, image, code, multimodal)
- Prompt engineering techniques: zero-shot, few-shot, chain-of-thought
- Retrieval-augmented generation (RAG) and grounding
- Fine-tuning and model customization approaches
- Embeddings and vector databases
- AI agents and agentic workflows
- Gen AI limitations: hallucinations, bias, data freshness
- Responsible AI principles: fairness, interpretability, privacy, security
High-Yield Exam Tips
- Expect scenario questions that test what generative ai is and how it differs from traditional ml in real Google Cloud contexts
- Map each topic above to hands-on labs or documentation — GCP-GAIL rewards applied knowledge
- After reading, complete practice questions for this domain before moving on
- Revisit weak areas using the Generative AI Leader study plan and flashcards
Practice This Domain
Test your understanding with free practice questions at /certifications/google-cloud/generative-ai-leader/practice — focus on topics: What generative AI is and how it differs from traditional ML, Large language models (LLMs): training, inference, tokens, and context windows.
