GCP Generative AI Leader Prep:
The Generative AI Leader study guide maps every objective of Google Cloud’s Generative AI Leader certification to official documentation, so you can prepare with confidence. It spans gen AI fundamentals, Google Cloud’s prebuilt offerings, techniques to improve model output, and the business strategies behind a successful gen AI solution.
Use it as a checklist to track your readiness across all four exam sections.
You can also explore more Google Cloud certification study guides on the GCP certification category to keep building your skills.
Google Generative AI Leader Materials
| Udemy | Be a Generative AI Leader – Google Cloud |
| Coursera | Generative AI Leader Professional Certificate |
| Whizlabs | Google Cloud Certified Generative AI Leader |
Section 1: Fundamentals of gen AI (~30% of the exam)
1.1 Describe core generative AI (gen AI) concepts and use cases. Considerations include:
Defining core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models).
Generative AI beginner’s guide
Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement).
Generative AI beginner’s guide
Identifying the stages of the machine learning lifecycle; data ingestion, data preparation, model training, model deployment, and model management; and the Google Cloud tools for each stage.
Introduction to Vertex AI Pipelines
Develop a generative AI application
Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost, performance, fine-tuning, and customization).
Overview of models on Agent Platform
Identifying business use cases where gen AI can create, summarize, discover, and automate (e.g., text generation, image generation, code generation, video generation, data analysis, and personalized user experience).
Develop a generative AI application
Describing how various data types are used in gen AI and the business implications.
Generative AI beginner’s guide
Develop a generative AI application
Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format).
Develop a generative AI application
Identifying the differences between structured and unstructured data, and identifying real-world examples of each type.
Identifying the differences between labeled and unlabeled data.
Generative AI beginner’s guide
1.2 Describe how various data types are used in gen AI and the business implications. Considerations include:
Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format).
Develop a generative AI application
Identifying the differences between structured and unstructured data, and identifying real-world examples of each type.
Identifying the differences between labeled and unlabeled data.
Generative AI beginner’s guide
1.3 Identify the core layers of the gen AI landscape and the business implications. Considerations include:
Infrastructure
Models
Overview of models on Agent Platform
Platforms
Agents
Applications
Develop a generative AI application
1.4 Identify the use cases and strengths of Google’s foundation models. Considerations include:
Gemini
Overview of models on Agent Platform
Gemma
Imagen
Veo
Section 2: Google Cloud’s gen AI offerings (~35% of the exam)
2.1 Describe Google Cloud’s strengths in the field of gen AI. Considerations include:
Describing how Google’s AI-first approach and commitment to future innovation translate into cutting-edge gen AI solutions.
Describing how Google Cloud has an enterprise-ready AI platform (e.g., responsible, secure, private, reliable, scalable).
Vertex AI shared responsibility
Recognizing the advantages of Google’s comprehensive AI ecosystem (e.g., integration of gen AI across Google products and services).
Describing the benefits of Google Cloud’s open approach.
Overview of models on Agent Platform
Identifying the essential components of Google Cloud’s AI-optimized infrastructure and its benefits (e.g., hypercomputer, Google’s custom-designed TPUs, GPUs, data centers, cloud computing).
Explaining how Google Cloud’s AI platform provides users with control over their data (e.g., security, privacy, governance, open and leading first party models, pre-built and customizable solutions, agents).
Vertex AI shared responsibility
Identity and Access Management (IAM)
Describing how Google Cloud’s AI platform democratizes AI development (e.g., low-code and no-code tools, pre-trained models, APIs).
2.2 Describe how Google Cloud’s prebuilt gen AI offerings enable AI powered work. Considerations include:
Recognizing the functionality, use cases, and business value of the Gemini app and Gemini Advanced (e.g., Gems).
Recognizing the functionality, use cases, and business value of Gemini Enterprise (e.g., Cloud NotebookLM API, multimodal search, and custom agent capabilities).
Recognizing the functionality, use cases, and business value of Gemini for Google Workspace.
2.3 Describe how Google Cloud’s gen AI offerings improve the customer experience. Considerations include:
Recognizing the functionality, use cases, and business benefits of Google Cloud’s external search offerings (e.g., Agent Search on Gemini Enterprise Agent Platform , Google Search).
Recognizing the functionality, use cases, and business value of Google’s Customer Engagement Suite (e.g., Conversational Agents, Agent Assist, Conversational Insights, Google Cloud Contact Center as a Service).
Customer Experience Agent Studio
2.4 Describe how Google Cloud empowers developers to build with AI. Considerations include:
Recognizing the functionality, use cases, and business value of Agent Platform (e.g., Model Garden, Agent Search, Agent Platform AutoML).
Recognizing the functionality, use cases, and business value of Google Cloud’s RAG offerings (e.g., prebuilt RAG with Agent Search, RAG APIs).
RAG Engine on Gemini Enterprise Agent Platform overview
Recognizing the functionality, use cases, and business value of using Agent Platform to build custom agents.
2.5 Define the purpose and types of tooling for gen AI agents. Considerations include:
Identifying how agents use tools to interact with the external environment and achieve tasks (e.g., extensions, functions, data stores, and plugins).
RAG Engine on Gemini Enterprise Agent Platform overview
Identifying relevant Google Cloud services and pre-built AI APIs for agent tooling (e.g., Cloud Storage, databases, Cloud Functions, Cloud Run, Agent Platform, Speech-to-Text API, Text-to-Speech API, Translation API, Document Translation API, Document AI API, Cloud Vision API, Cloud Video Intelligence API, Natural Language API, Google Cloud API Library).
Determining when to use Agent Studio and Google AI Studio.
Overview of prompting strategies
Section 3: Techniques to improve gen AI model output (~20% of the exam)
3.1 Describe how to proactively overcome foundation model limitations. Considerations include:
Identifying common limitations of foundation models (e.g., data dependency, the knowledge cutoff, bias, fairness, hallucinations, edge cases).
RAG Engine on Gemini Enterprise Agent Platform overview
Describing the Google Cloud-recommended practices to address limitations (e.g., grounding, retrieval-augmented generation [RAG], prompt engineering, fine-tuning, human in the loop [HITL]).
RAG Engine on Gemini Enterprise Agent Platform overview
Overview of prompting strategies
Recognizing Google-recommended practices for continuous monitoring and evaluation of gen AI models (e.g., automatic model upgrades, key performance indicators, security patches and updates, versioning, performance tracking, drift monitoring, Agent Platform Feature Store).
3.2 Describe prompt engineering techniques and how they drive better results. Considerations include:
Defining prompt engineering and describing its significance in interacting with large language models (LLMs).
Overview of prompting strategies
Identifying prompting techniques and use cases (e.g., zero-shot, one-shot, few-shot, role prompting, prompt chaining).
Overview of prompting strategies
Generative AI beginner’s guide
Identifying advanced prompting techniques and when to use them (e.g., chain-of-thought prompting, ReAct prompting).
Overview of prompting strategies
3.3 Identify grounding techniques and their use cases. Considerations include:
Describing the concept of grounding in LLMs and differentiating between grounding with first-party enterprise data, third-party data, and world data.
RAG Engine on Gemini Enterprise Agent Platform overview
Describing how retrieval-augmented generation (RAG) can affect the generated output from your gen AI models.
RAG Engine on Gemini Enterprise Agent Platform overview
Google Cloud grounding offerings: Pre-built RAG with Agent Search, RAG APIs, and Grounding with Google Search.
RAG Engine on Gemini Enterprise Agent Platform overview
Identifying how sampling parameters and settings are used to control the behavior of gen AI models (e.g., token count, temperature, top-p [nucleus sampling], safety settings, and output length).
Overview of prompting strategies
Section 4: Business strategies for a successful gen AI solution (~15% of the exam)
4.1 Describe the Google Cloud-recommended steps to successfully implement a transformational gen AI solution. Considerations include:
Recognizing the different types of gen AI solutions (e.g., text generation, image generation, code generation, personalized user needs).
Develop a generative AI application
Identifying the key factors that influence gen AI needs (e.g., business requirements, technical constraints).
Develop a generative AI application
Describing how to choose the right gen AI solution for a specific business need.
Develop a generative AI application
Identifying the steps to integrate gen AI into an organization.
Develop a generative AI application
Identifying techniques to measure the impact of gen AI initiatives.
Develop a generative AI application
4.2 Define secure AI and its importance in protecting AI systems from malicious attacks and misuse. Considerations include:
Explaining security throughout the ML lifecycle.
Vertex AI shared responsibility
Identifying the purpose and benefits of Google’s Secure AI Framework (SAIF).
Vertex AI shared responsibility
Recognizing Google Cloud security tools and their purpose (e.g., secure-by-design infrastructure, Identity and Access Management (IAM), Security Command Center, and workload monitoring tools).
Identity and Access Management (IAM)
Vertex AI shared responsibility
4.3 Describe the importance of responsible AI in business. Considerations include:
Explaining the importance of responsible AI and transparency.
Describing privacy considerations (e.g., privacy risks, data anonymization and pseudonymization).
Vertex AI shared responsibility
Describing the implications of data quality, bias, and fairness.
Introduction to Vertex Explainable AI
Describing the importance of accountability and explainability in AI systems.
Introduction to Vertex Explainable AI
Vertex AI shared responsibility
Generative AI Leader Study Guide – Final Thoughts
This Generative AI Leader study guide walked through every section of the exam, from gen AI fundamentals and Google Cloud’s prebuilt offerings to techniques for improving model output and the business strategies behind successful adoption. Working through each objective and its official documentation links is one of the most effective ways to build the conceptual fluency this certification rewards.
Keep revisiting the areas that feel least familiar, and you’ll walk into the exam well prepared.
You can also explore more Google Cloud certification study guides on the GCP certification category to keep building your skills. Have a question or tip? Leave a comment below.
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