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Google Cloud Generative AI Leader

Google's business-level generative AI certification. No prerequisites, no coding, no console work: it tests whether you can reason about generative AI as a business capability, name what Google Cloud offers, and run an adoption program responsibly.

Every other AI certification in this repo is aimed at people who build. This one is aimed at people who decide whether to build, which is a genuinely different and underserved audience.

Exam Details

  • Duration: 90 minutes
  • Questions: 50-60, multiple choice and multiple select
  • Cost: USD 99
  • Validity: 3 years
  • Prerequisites: None
  • Format: Knowledge-based; no code, no console

Full detail in the fact sheet.

Notes

Notes Covers
01 Generative AI fundamentals Foundation models, tokens, context, multimodality, hallucination, agents
02 Google Cloud's offerings Vertex AI, Gemini, Gemma, Imagen, Agent Builder, Vertex AI Search, applied assistants
03 Improving output Prompting, parameters, grounding, RAG, fine-tuning, function calling, evaluation
04 Business strategy Use case selection, business case, TCO, change management, responsible AI

The two habits that pass this exam

1. Reach for the lowest rung of the ladder. Google frames adoption as a progression: use an applied assistant, then a pre-built API, then build on the platform, then customize a model, then train from scratch. Cost and effort rise sharply at each step. A question describing a routine need answered by "fine-tune a custom model" is almost always wrong.

2. Match the technique to the problem.

Problem Answer
The model does not know about our data Grounding or RAG
The model does not know current facts Grounding with Google Search
The output format or tone is inconsistent Prompting first, then fine-tuning if it persists
The model needs to take an action or read a live system Function calling
The output is too random Lower the temperature
We do not know whether it is good enough Evaluation

The recurring wrong answer is fine-tuning applied to a knowledge problem.

Study sequence

  1. Fundamentals, which most readers of this repo will already know
  2. Google's product portfolio, which is the main memorization load
  3. Techniques for improving output
  4. Business strategy and responsible AI, which is more heavily weighted than a technical reader expects

Schedule in the practice plan.

Study resources