Study Guide
Generative AI Leader Study Guide
Use the official section outline to connect gen AI vocabulary, Google Cloud offerings, prompt and grounding techniques, and business implementation strategy to scenario-based questions.
How the Exam Is Structured
The Google Cloud Generative AI Leader exam validates business-level knowledge of generative AI concepts, Google Cloud gen AI offerings, output-improvement techniques, and adoption strategy. The ExamPal practice bank includes 367 premium questions and 40 free questions mapped across the official blueprint.
| Domain | Weight | Focus |
|---|---|---|
| Section 1: Fundamentals of gen AI | 30% | Describe core generative AI (gen AI) concepts and use cases; Describe how various data types are used in gen AI and the business implications |
| Section 2: Google Cloud’s gen AI offerings | 35% | Describe Google Cloud's strengths in the field of gen AI; Describe how Google Cloud’s prebuilt gen AI offerings enable AI powered work |
| Section 3: Techniques to improve gen AI model output | 20% | Describe how to proactively overcome foundation model limitations; Describe prompt engineering techniques and how they drive better results |
| Section 4: Business strategies for a successful gen AI solution | 15% | Describe the Google Cloud-recommended steps to successfully implement a transformational gen AI solution; Define secure AI and its importance in protecting AI systems from malicious attacks and misuse |
30% of exam
Section 1: Fundamentals of gen AI
Covers the foundational concepts, data considerations, landscape layers, and Google foundation models that underpin generative AI in business contexts. This section emphasizes conceptual understanding, use-case identification, and strategic evaluation rather than technical implementation.
35% of exam
Section 2: Google Cloud’s gen AI offerings
Covers Google Cloud’s generative AI portfolio, platform strengths, prebuilt offerings, customer experience solutions, developer tools, and agent tooling. This section emphasizes how Google Cloud positions its AI platform, infrastructure, and products for enterprise use and AI-powered work.
20% of exam
Section 3: Techniques to improve gen AI model output
Covers methods for improving model output quality, including overcoming foundation model limitations, prompt engineering, grounding, retrieval-augmented generation, and sampling controls. The section also includes monitoring and evaluation practices for gen AI models.
15% of exam
Section 4: Business strategies for a successful gen AI solution
Covers business and organizational considerations for implementing gen AI solutions, including solution selection, integration, impact measurement, security, and responsible AI. The section emphasizes secure AI practices, SAIF, and responsible AI principles such as transparency, privacy, bias, fairness, accountability, and explainability.
Key Terms to Know
These terms are loaded from the shared terminology pack and appear across the question explanations.
- AI
- Abbreviation for artificial intelligence.
- AI on the edge
- Running AI solutions on infrastructure such as devices or servers closer to where the action is happening.
- AI/ML
- An acronym used in the text to refer to artificial intelligence and machine learning in the context of model risks and security.
- Agent Assist
- A tool that supports live human contact center agents.
- AutoML
- A way to create and train models with minimal technical knowledge and effort.
- Bias
- A limitation in which models learn patterns from large datasets that may contain bias, and even subtle biases can be magnified in outputs.
- BigQuery
- A Google Cloud data service that Gemini for Google Cloud can use to analyze data.
- Cloud Video Intelligence API
- A Google Cloud API that analyzes video content and extracts meaningful information for uses such as content recommendation, video search, and media analysis.
- Cloud Vision API
- A Google Cloud API that analyzes image content, tags images based on detected objects and text, identifies faces and landmarks, and supports content moderation and visual search.
- CoT
- An acronym for chain-of-thought, a prompting technique that guides an LLM through a problem-solving process by providing examples with intermediate reasoning steps.
- Contact Center as a Service (CCaaS)
- An enterprise-grade contact center solution that is native to the cloud and can underpin customer engagement tools.
- Content moderation
- A human-in-the-loop use case in which user-generated content is moderated contextually so harmful material that algorithms might overlook can be caught.
- Context window
- The amount of text a model can consider at one time.
- Conversational Agents
- Tools that act as chatbots for customers.
- Conversational Insights
- A tool for gaining insights into communications with customers.
- Data dependency
- A limitation in which foundation model performance relies heavily on the quality and completeness of the training data; biased or incomplete data affects outputs.
- Data stores
- Tooling that provides access to information.
- Document AI API
- A Google Cloud API that extracts data from varied formats, automates data capture and document processing, and can summarize documents.
Official Materials and Guidance
This page is built from Google Cloud official exam guide and study guide, the shared syllabus, topic tree, terminology pack, free pack, and premium pack.
- -Generative AI Leader exam guide PDF
- -Generative AI Leader study guide PDF