Google Cloud Generative AI Leader Exam Prep: Free Course

The Google Cloud Generative AI Leader Exam Preparation course is a free, foundational video course that prepares you for the Google Cloud Generative AI Leader certification. It is built for people who need to understand and lead generative AI work in a business, not for people who write model code every day.

Over 18 short labs, the course moves from the basics of generative AI to the Google Cloud products, agents, prompting, responsible AI and the business case for adoption. Each lab is about ten minutes long, so you can fit one into a lunch break. You can see the full course on the Generative AI Leader course page.

Who this course is for

The course is labelled Foundational. It suits:

Managers, team leads and product people who must decide where generative AI fits in their organisation.
Business analysts and consultants who talk to clients about AI use cases.
Students and career changers who want a first Google Cloud AI certification.
Technical people who know some AI but want the leader’s view: value, risk, adoption and the Google Cloud product map.

You do not need to be a programmer. What helps most is curiosity about how AI creates value and an interest in how organisations adopt new tools.

What you will learn

The videos group naturally into six themes.

1. Generative AI foundations. Labs 02 to 04, Generative Versus Traditional AI, Models, Training and Inference and Data Types and Readiness, explain what makes generative AI different, how a model is trained and then used, and why the quality and readiness of your data matter before any project starts.

2. Risks and use cases. What Goes Wrong looks at the ways generative AI can fail, and Finding and Ranking Use Cases shows how to choose which problems are worth solving first. A leader who can spot weak use cases early saves a lot of time and budget.

3. The Google Cloud product landscape. Six Categories, Twenty-Six Products gives you a map of the Google Cloud generative AI offering. Choosing a Model and Where Building Happens then explain how to pick a model and where solutions are actually built. This is the part many learners find hardest, because there are so many product names.

4. Agents and employee tools. Agents on Google Cloud covers agents, and What Employees Use Directly covers the AI tools that staff use in their daily work. Grounding Data and Hardware explains what sits underneath those experiences.

5. Getting better answers. Prompting Techniques, Grounding the Answer and Improving and Measuring Output teach how to write better prompts, connect answers to trusted data, and measure whether the output is good enough.

6. Responsible AI and business value. Principles and Practice covers responsible AI, and Value, KPIs and Adoption covers how to measure success and bring people along. The course closes with Capstone and Exam Preparation.

How the course is organised

The course has 19 videos and runs for about 3.3 hours in total.

Overview (6 minutes): what the 18 labs cover and how they fit together.
Lab 01, Setup and How to Study This Exam: how to approach your preparation.
Labs 02 to 17: one focused topic per video, each around 9 to 12 minutes.
Lab 18, Capstone and Exam Preparation (20 minutes): the longest video, pulling the whole course together.

The order matters. The early labs give you the vocabulary that the product and agent labs rely on, and the responsible AI and value labs make most sense once you know what the products do.

How to study it step by step

Watch the overview and Lab 01 first. They set out the structure of the course and how to study, so you know where you are heading.
Do Labs 02 to 05 in one or two sittings. These are the foundations. Write a one-line definition for every new term you meet.
Spend extra time on Labs 06 to 09. Make your own table of the product categories from Six Categories, Twenty-Six Products, and add a note on when you would choose each one. Re-watch Choosing a Model if the choices feel blurred.
Connect agents to real work. While watching Labs 10 to 12, think of one task in your own job that an agent or an employee AI tool could help with.
Practise prompting. After Labs 13 to 15, try the techniques on any AI assistant you have access to and compare the results.
Finish with the leader topics. Labs 16 and 17 cover responsible AI, KPIs and adoption. Write down how you would measure success for the use case you picked in step 4.
Use the capstone as a review. Watch Lab 18 last, pause where you feel unsure, and go back to the matching lab.

A sensible pace is two or three labs a day, which finishes the course in about a week. You can follow the whole series in order on the YouTube playlist.

Frequently asked questions

Is the Google Cloud Generative AI Leader course free?

Yes. All 19 videos are free to watch on YouTube. You can start from the course page or go straight to the playlist.

How long does it take to finish?

The videos add up to about 3.3 hours. If you pause to take notes and re-watch the product labs, plan for roughly a week at two or three labs a day.

Do I need coding skills or a Google Cloud account?

The course is foundational and aimed at leaders, so it focuses on concepts, products, use cases and adoption rather than writing code. You can follow it by watching, and try the prompting ideas on any AI assistant you already use.

Which topics does the course cover for the exam?

It covers generative versus traditional AI, models, training and inference, data readiness, what goes wrong, use cases, the Google Cloud product categories, choosing a model, agents, prompting, grounding, measuring output, responsible AI, and value, KPIs and adoption, ending with a capstone and exam preparation video.