Google Cloud Certified Generative AI Leader

Exam code: GENAI-LEADER · Google Cloud

Understand AI that creates text, images, or other content, and how businesses can use it with Google Cloud's tools.

Who it suits: Business professionals exploring content-generating AI, including managers, marketers, and consultants.
Experience to bring: No technical background or formal prerequisites required. The exam includes Google Cloud products.

A workplace example Compare ways to help a team draft content or search company information, while checking answer quality.

This is independent study material. To earn the certification, book and pass the provider's official exam separately.

You can register directly with the exam provider. No prerequisites. Online-proctored or onsite.

Practice — 12 questions Read the study guide Official exam guide →

Exam facts

Questions	50-60 multiple-choice items	Includes questions with one or several correct answers
Time limit	90 min	Taken under supervision
Passing score	Not published
Fee	$99	Per attempt
How long it lasts	36 months	Renew within the eligibility period before expiry

What the exam covers

The provider divides the exam into 4 topic groups, called domains. The percentage shows how much of the exam each group covers. Topic names below match the official guide.

	Topic group	Share of exam	Questions available here
1	Fundamentals of Generative AI	30.0%	3
2	Google Cloud Generative AI Offerings	35.0%	3
3	Improving Model Output	20.0%	3
4	Business Strategy for Generative AI	15.0%	3

Google Cloud does not publish a question count for this exam, so we do not show a full practice exam target. Questions available here is the number you can practice on OutManage. Some topics have less material than others; these counts show what is ready now.

More detail about the exam information

Cheapest entry point and the least vendor-locked of the three: roughly half the blueprint is general AI literacy. Three-year validity. Google publishes these section weights as approximate (~30/~35/~20/~15), not as exact points; they happen to sum to 100, so no normalization was needed.

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