Generative AI Leader exam: format, sections and how to prepare

Who the Generative AI Leader exam is for

The Google Cloud exam page states that the certification "is for anyone in any job role, with or without hands-on technical experience", and lists no prerequisite. The exam guide describes the certified professional as someone with business-level knowledge of Google Cloud's gen AI products and services, who can talk with both technical and non-technical teams and identify use cases across business functions. It adds that the expertise is "in strategic leadership and influence, not technical implementation", although a conceptual understanding of gen AI is expected.

In practice, expect questions that start from a business situation and ask which concept, technique or Google Cloud offering fits it, not questions about code or configuration.

Exam format

The exam page does not publish a passing score, so do not rely on a figure quoted elsewhere. It also states that the exam was recently updated to reflect branding changes, and tells candidates to refer to the exam guide for the product names used on the exam.

Sections and weights

The exam guide defines four sections, each with an approximate weight and numbered objectives. It gives no weight per objective.

Points the guide spells out

• Foundation model choice: modality, context window, security, availability and reliability, cost, performance, fine-tuning and customization.
• Data: quality and accessibility (completeness, consistency, relevance, availability, cost, format), structured versus unstructured, labeled versus unlabeled.
• Limitations and remedies: data dependency, knowledge cutoff, bias, hallucinations, then grounding, retrieval-augmented generation (RAG), prompt engineering, fine-tuning and human in the loop.
• Prompting: zero-shot, one-shot, few-shot, role prompting, prompt chaining, then chain-of-thought and ReAct.
• Grounding: first-party enterprise data, third-party data and world data, and sampling parameters such as temperature and top-p.
• Security and responsibility: security across the ML lifecycle, Google's Secure AI Framework (SAIF), privacy, bias, accountability and explainability.

The second section weighs the most and is the section that names the most products. The exam page warns that names changed recently, so study the current names in the exam guide rather than older course notes.

How to prepare

1. Read the Generative AI Leader exam page on Google Cloud for registration, languages and current conditions.
2. Read the exam guide (PDF) and copy each numbered objective into a checklist. The guide is five pages long, so this takes one sitting.
3. Follow the Generative AI Leader learning path linked from the exam page, and skim the study guide it also links.
4. Build a short glossary first: foundation model, multimodal model, diffusion model, prompt tuning, grounding, RAG, hallucination. The first and third sections rely on this vocabulary.
5. For each Google Cloud offering named in section 2, write one line on the business problem it solves and what distinguishes it from the closest one, for instance Gemini Enterprise versus Gemini for Google Workspace.
6. Practise the "which approach fits this situation" reasoning: when grounding or RAG is the answer, when prompt engineering is enough, when fine-tuning is justified.
7. Take our Generative AI Leader practice exam under timed conditions, then review each missed question in the free study mode.
8. Shortly before the exam, re-read the exam page in case the product names or conditions have changed.