Google Cloud Certified Generative AI Leader

A credential for people who decide what an organisation does with generative AI rather than build it. Google states plainly that the expertise tested is strategic leadership and influence, not technical implementation, which makes it the closest thing to a product certification any cloud vendor offers.

At a glance
Cost
$99 Google lists the exam at $99 plus tax where it applies. There is no separate registration fee and a retake is charged in full. Google publishes a waiting period between attempts that lengthens with each failure.
Format
50 to 60 multiple choice questions in 90 minutes. Taken online with a proctor or at a test centre. Offered in English, Japanese, Spanish and Portuguese.
Pass mark
Google does not publish one. Its certifications report only a pass or a fail with no score, and no breakdown by section, so there is no figure to aim at beyond passing.
Prerequisites
None. Google states the exam is for anyone in any job role, with or without hands on technical experience, and recommends familiarity with generative AI concepts rather than any prior certification.
Renewal
Valid for three years. Recertification is by taking the exam again, and Google publishes a renewal window opening some months before expiry.
Based on
The published Generative AI Leader exam guide, which sets out four sections with approximate weightings and lists the considerations under each
What it covers

What it covers. This is the vendor AI certification aimed squarely at people who do not write the code. Google's own description of the candidate is somebody who can hold a conversation with technical and non technical teams, spot use cases across business functions, and influence what gets funded. Nothing on the exam asks you to build anything.

The heaviest section is Google's own offerings at 35 per cent, and it is the section that ages fastest. Google renamed the platform this exam is built on, so Vertex AI is now Gemini Enterprise Agent Platform and Vertex AI Search is now Agent Search. Older study material still uses the previous names and will cost you marks.

A fifth of the exam is techniques for improving output, covering grounding, retrieval, prompting and sampling parameters. This is the most transferable material on the exam and the part worth learning even if you never sit it.

The smallest section, business strategy at 15 per cent, carries the Secure AI Framework and responsible AI. Product people tend to find this the easiest reading and skip it, which is a poor trade on a section where the questions are answerable from a page of notes.

The syllabus4 domains · free

Google Cloud’s own domains. Each opens its own page listing the concepts beneath it, and each concept has a page of its own.

01Fundamentals of Gen AIThe vocabulary, the three ways a model learns, the machine learning lifecycle, how a foundation model is chosen for a use case, what data quality means in practice, the five layers of the landscape, and Google's own models.Supervised, unsupervised and reinforcement learning · The machine learning lifecycle · Foundation models and how they work · Choosing and configuring a foundation model · Data for generative AI · The five layers of the gen AI landscape · Google's foundation models7 concepts
~30%
02Google Cloud's Gen AI OfferingsThe heaviest section. Google's platform and the infrastructure under it, Gemini across the product range, the customer experience offerings, Agent Platform for building, and how an agent reaches the outside world through tools.Google Cloud's AI platform · Gemini across Google's products · Customer experience with gen AI · Building with Agent Platform · Agents and their tools5 concepts
~35%
03Techniques to Improve Gen AI Model OutputThe limits a foundation model arrives with, grounding against first party, third party and world data, prompting from zero shot through ReAct, the sampling parameters that shape a response, and customisation.What generative AI is good and bad at · Grounding and retrieval · Prompt engineering · Customising a foundation model4 concepts
~20%
04Business Strategies for a Successful Gen AI SolutionChoosing a solution for a business need and measuring whether it worked, the Secure AI Framework and the tools around it, and responsible AI as something a business is accountable for rather than a principle it states.Adopting generative AI in a business · Secure AI and the Secure AI Framework · Responsible AI in practice · Transparency and explainability4 concepts
~15%
Try a question

A retailer wants a customer service assistant that answers from its own returns policy, checks an order in its warehouse system, and issues a refund where the policy allows it. Which layer of the gen AI landscape does this describe?

  • AModels, because a foundation model produces every response.
  • BAgents, because the system plans steps, calls tools and acts rather than only answering.
  • CInfrastructure, because it requires compute to serve requests.
  • DApplications, because customers interact with it directly.
60 questions across the 4 domains.

One per page, with a worked explanation.

Start the set