A marketing team's assistant keeps answering questions about the company's current promotions with details that are months out of date. Which technique most directly fixes this?

Ground the model in the company's current promotions data at query time

Increase the model's temperature

Ask the model to be more careful in the system prompt

An executive wants to pick the first generative AI use case for a company. Which choice is most likely to succeed as a first project?

A well-bounded task with measurable output, available data and a clear owner

The most ambitious use case, to show the technology's full potential

A customer-facing use case, so the impact is visible externally

A use case chosen by the vendor

Who is the Generative AI Leader for?

Business and product people who make or influence generative AI decisions. Engineers should look at the Professional Machine Learning Engineer instead.

Conceptually yes, in that you must understand how the models behave; practically no, there is no coding or architecture.

Fifteen to thirty hours, more if the Google Cloud product names are new to you.

What is the pass mark?

Google does not publish one; results are reported as pass or fail. Aim for consistent 80% on practice papers.

Cloud Digital Leader or Generative AI Leader?

Digital Leader if you need the whole Google Cloud picture; Generative AI Leader if generative AI is your job. Many people take both.

All certifications

Google Cloud · Foundational

Google Cloud Generative AI Leader

Google Cloud's business-facing generative AI credential: what the models do, how Google Cloud packages them, and how to run a gen AI programme.

Free practice: 213 questions written to the official exam guide, with the diagnostic, practice papers, timed mocks, a study plan and an explanation on every answer.

Official exam page

Find out where you stand on Google Cloud Generative AI Leader

20 questions drawn in the exam’s own domain weights. You get a report straight away: your score against the pass mark, where the points went, and what to study first.

Questions: 20
Time: about 20 minutes
Cost: free

Your email

Your answers save as you go, and the report stays in your record. A new email needs no code.

· your answers stay on this device and you can save the report at the end.

The course

The diagnostic shows where you stand. Then you learn the syllabus chapter by chapter, answer that chapter’s questions, and move on. When every chapter is mastered, you sit full timed mocks in the real exam’s shape.

13 chapters, about 10 hours in all. Each is a 45-minute study session with quick checks, then a 10-question quiz on that chapter.

Fundamentals of generative AI30% of the exam

Core gen AI and machine learning concepts45 min
Data for gen AI45 min
The gen AI landscape and Google's foundation models45 min

Google Cloud's generative AI offerings35% of the exam

Google Cloud's strengths in gen AI45 min
Prebuilt gen AI for everyday work45 min
Gen AI for customer experience45 min
Building with Agent Platform and RAG45 min
Agents and their tools45 min

Techniques to improve gen AI model output20% of the exam

Overcoming foundation model limitations45 min
Prompt engineering45 min
Grounding, RAG and sampling settings45 min

Business strategies for a successful gen AI solution15% of the exam

Implementing a transformational gen AI solution45 min
Secure AI and responsible AI45 min

About the Google Cloud Generative AI Leader exam

The Generative AI Leader is Google Cloud's business-facing generative AI certification. It is for people who lead or influence generative AI decisions, in product, strategy, operations or leadership, rather than for engineers. It tests whether you understand what generative AI can and cannot do, how Google Cloud packages it (Gemini, Vertex AI, Agentspace, the model garden), and how to run a generative AI programme that delivers.

There is no coding and no architecture. The questions are scenario-based: a business situation, and which capability, technique or approach fits. The largest domain is Google Cloud's own offerings, so knowing the product landscape by name and purpose matters more than on most foundational exams.

It sits alongside the Cloud Digital Leader as a no-prerequisite entry point, and it is the one to take if your interest is generative AI specifically.

The exam at a glance

Questions: 50 to 60 questions, multiple choice and multiple select
Time: 90 minutes
Pass mark: Not published by Google; pass or fail is reported
Exam fee: About US$99
Valid for: 3 years
Where you sit it: Online proctored or test centre
Prerequisites: None

As published in the vendor’s exam guide at the time of writing. Fees vary by country and change; the vendor’s page is the source of truth.

What is on the exam, domain by domain

Fundamentals of generative AI
Foundation models, multimodality, tokens and context, prompts, hallucination, and the differences between generative and traditional AI.
30% of the paper
Google Cloud's generative AI offerings
The largest domain. Gemini models and apps, Vertex AI, Model Garden, Agentspace, NotebookLM, grounding with Google Search, and which offering fits a described need.
35% of the paper
Techniques to improve gen AI model output
Prompt engineering patterns, grounding and retrieval, fine-tuning, human feedback, and evaluation.
20% of the paper
Business strategies for a successful gen AI solution
Use-case selection, responsible AI, data governance, measuring value, change management and the risks of a pilot that never scales.
15% of the paper

How to prepare

Start with Google's free Generative AI Leader learning path on Google Cloud Skills Boost; the exam follows it closely.
Build a one-page map of the Google Cloud generative AI products and the single sentence each one exists for. That map is 35% of the paper.
Learn the vocabulary of output quality: prompting, grounding, retrieval, fine-tuning, evaluation, guardrails. Questions ask which technique fixes which problem.
Read Google's responsible AI principles and its published guidance on gen AI governance; the business domain leans on them.
Sit a diagnostic, study the weak domains, then sit two timed practice papers before you book.

Sample questions

Three questions in the exam’s style, written by Square 1 against the published domains. They are ours, not the vendor's. The answer and the reasoning are under each one; the practice papers on this page draw from a bank written the same way, with an explanation on every question.

Question 1 · Techniques to improve gen AI model output
A marketing team's assistant keeps answering questions about the company's current promotions with details that are months out of date. Which technique most directly fixes this?
1. Increase the model's temperature
2. Ground the model in the company's current promotions data at query time
3. Ask the model to be more careful in the system prompt
4. Switch to a smaller model
Answer: B. The model's training data is stale; grounding supplies current facts at query time so answers are drawn from them. Temperature changes randomness, a smaller model knows less, and asking for care does not give the model information it lacks.
Question 2 · Google Cloud's generative AI offerings
A team wants to build and deploy a custom generative AI application on Google Cloud, choosing among first-party and open models, with tools for tuning and evaluation. Which platform is designed for this?
1. Google Workspace
2. Vertex AI
3. Cloud Storage
4. Looker
Answer: B. Vertex AI is Google Cloud's platform for building, tuning, evaluating and deploying generative and predictive AI, with access to Gemini and the Model Garden. Workspace is productivity software, Cloud Storage holds objects, and Looker is business intelligence.
Question 3 · Business strategies for a successful gen AI solution
An executive wants to pick the first generative AI use case for a company. Which choice is most likely to succeed as a first project?
1. The most ambitious use case, to show the technology's full potential
2. A well-bounded task with measurable output, available data and a clear owner
3. A customer-facing use case, so the impact is visible externally
4. A use case chosen by the vendor
Answer: B. First projects succeed when they are bounded, measurable, fed by data the company already has, and owned by someone accountable. Ambition and external visibility raise the cost of the failures a first project will have.

Questions people ask before they book

Who is the Generative AI Leader for?: Business and product people who make or influence generative AI decisions. Engineers should look at the Professional Machine Learning Engineer instead.
Is it technical?: Conceptually yes, in that you must understand how the models behave; practically no, there is no coding or architecture.
How long to prepare?: Fifteen to thirty hours, more if the Google Cloud product names are new to you.
What is the pass mark?: Google does not publish one; results are reported as pass or fail. Aim for consistent 80% on practice papers.
Cloud Digital Leader or Generative AI Leader?: Digital Leader if you need the whole Google Cloud picture; Generative AI Leader if generative AI is your job. Many people take both.