Exam Code: Generative-AI-Leader
Exam Name: Google Cloud Certified - Generative AI Leader Exam
Certification Provider: Google
Corresponding Certification: Google Cloud Certified

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Updated: Aug 09, 2026

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Google Generative-AI-Leader Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Generative AI Leader
Exam Number:	Generative-AI-Leader
Exam Duration:	90 minutes
Exam Price:	$99 USD
Related Certifications:	Google Cloud Digital Leader Google Cloud Professional Machine Learning Engineer
Certificate Validity Period:	2 years
Exam Format:	Multiple choice, Multiple select
Available Languages:	English
Real Exam Qty:	50-60
Recommended Training:	Google Cloud Skills Boost - Generative AI learning paths
Exam Registration:	Google Cloud Certification Portal
Sample Questions:	Google Generative-AI-Leader Sample Questions
Exam Way:	Online proctored exam
Pre Condition:	No strict prerequisites; basic understanding of cloud computing and AI concepts recommended
Official Syllabus URL:	https://cloud.google.com/learn/certification/generative-ai-leader

Google Generative-AI-Leader Exam Syllabus Topics:

Topic	Details
Topic 1	Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.
Topic 2	Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
Topic 3	Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.
Topic 4	Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.

Reference: https://cloud.google.com/developers/generative-ai-leader

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