Chapter 1 of 4 · Google Cloud Generative AI Leader
Fundamentals of gen AI
Gen AI concepts, ML approaches and lifecycle, data for AI, the gen AI landscape and Google models.
- 6topics
- 24 minfast track
- 3diagrams and exhibits
- 30%of the exam
Topics in this chapter
Take them in order, or jump to the one you need.
- 1Core Gen AI ConceptsGenerative AI Leader definitions: AI, NLP, machine learning, gen AI, foundation and multimodal models, LLMs, diffusion models…4 min
- 2Machine Learning Approaches and the ML LifecycleSupervised, unsupervised and reinforcement learning, and the ML lifecycle from data ingestion to model management with the Google…Diagram4 min
- 3Choosing a Foundation Model and Gen AI Use CasesHow to choose a foundation model by modality, context window, security, reliability, cost, performance and customization, and…4 min
- 4Data for Gen AIHow data is used in gen AI: data quality and accessibility, structured versus unstructured data, labeled versus unlabeled data…4 min
- 5The Gen AI LandscapeThe five layers of the gen AI landscape, infrastructure, models, platforms, agents and applications, their Google Cloud examples…Diagram4 min
- 6Google's Foundation ModelsUse cases and strengths of Google's foundation models for the Generative AI Leader exam: Gemini, the open Gemma models, Imagen…Diagram4 min
About this chapter
What this chapter covers
This section builds the vocabulary for everything else in the exam and is worth about 30 percent of it. It covers core gen AI concepts and use cases (definitions, machine learning approaches, the ML lifecycle, choosing a foundation model, what gen AI can do for a business), how data is used in gen AI (data quality and accessibility, structured and unstructured, labeled and unlabeled), the layers of the gen AI landscape (infrastructure, models, platforms, agents, applications) and Google's foundation models (Gemini, Gemma, Imagen and Veo).
The exam is written for leaders, not engineers. You need to explain the concepts clearly and connect them to business value, not build models.
How the topics fit together
- Core gen AI concepts covers AI, NLP, ML, gen AI, foundation models, LLMs, diffusion models, prompt engineering and prompt tuning.
- Machine learning approaches and the ML lifecycle covers supervised, unsupervised and reinforcement learning, and the lifecycle stages with Google Cloud tools.
- Choosing a foundation model and gen AI use cases covers selection factors and the create, summarize, discover and automate use cases.
- Data for gen AI covers data quality, accessibility, structured and unstructured, and labeled and unlabeled data.
- The gen AI landscape covers the five layers and their business implications.
- Google's foundation models covers Gemini, Gemma, Imagen and Veo.
Many questions describe a business goal and ask for the concept or model behind it. Learn the plain-language definition of each term first.
How to use these pages
Each topic page has the same shape, so you can skim for what you need.
- What it is: the idea in plain words.
- How it works: the mechanism, with examples and tables.
- What to remember for the exam: the facts to memorise and how questions are phrased.
- Common mistakes: the classic traps.
- Check yourself: three questions to test your understanding.
Ready for more practice?
The full Google Cloud Generative AI Leader question bank has 512 questions, with timed practice exams and an explanation for every answer.
This study guide is independent and is not affiliated with, endorsed by, or sponsored by Google Cloud. Vendor names, certification names and exam codes are trademarks of their respective owners and are used for identification only.