Google Cloud Generative AI Leader Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Fundamentals of gen AI (30% of the exam)

Describe core generative AI (gen AI) concepts and use cases.

• Defining core gen AI concepts (artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models).
• Describing the machine learning approaches (supervised, unsupervised, reinforcement).
• 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.
• Identifying how to choose the appropriate foundation model for a business use case (modality, context window, security, availability and reliability, cost, performance, fine-tuning, and customization).
• Identifying business use cases where gen AI can create, summarize, discover, and automate (text generation, image generation, code generation, video generation, data analysis, and personalized user experience).
• Describing how various data types are used in gen AI and the business implications.
• Explaining the characteristics and importance of data quality and data accessibility in AI (completeness, consistency, relevance, availability, cost, format).
• Identifying the differences between structured and unstructured data, and identifying real-world examples of each type.
• Identifying the differences between labeled and unlabeled data.

Summary: This section establishes the foundational language of generative AI. You’ll learn core terms, the machine learning approaches, and how Google Cloud supports each stage of the ML lifecycle. Equally important, you’ll explore how to align the right type of model with specific business needs, weighing considerations such as cost, tuning options, reliability, and performance.

By mastering these basics, you will be equipped to clearly communicate the value of generative AI initiatives across teams and identify relevant solutions. Beyond terminology, the focus is on connecting business opportunities with different generative AI use cases including code generation, text summarization, advanced data analysis, and personalized experience delivery.

Describe how various data types are used in gen AI and the business implications.

• Explaining the characteristics and importance of data quality and data accessibility in AI (completeness, consistency, relevance, availability, cost, format).
• Identifying the differences between structured and unstructured data, and identifying real-world examples of each type.
• Identifying the differences between labeled and unlabeled data.

Summary: This area focuses on the very fuel that powers AI: data. You’ll assess the impact of different data types, such as structured databases or unstructured image archives, and recognize the importance of labeled versus unlabeled information. Central here is understanding why accessibility, quality, and cost matter for reliable results.

As leaders, having fluency in these distinctions enables you to set expectations for AI-driven initiatives across the enterprise. You’ll be able to translate technical details into meaningful guidance for stakeholders and ensure that the right groundwork is set for model training, deployment, and refinement.

Identify the core layers of the gen AI landscape and the business implications.

• Infrastructure
• Models
• Platforms
• Agents
• Applications

Summary: Here you will explore the layered structure of the generative AI ecosystem, gaining clarity on the role each layer plays in delivering solutions. Infrastructure, models, platforms, agents, and applications collectively form the building blocks that enable AI deployment. By familiarizing yourself with these layers, you build the foundation to understand how capabilities scale and connect.

The business value lies in recognizing the impact of each layer across different organizational priorities. Leaders can make stronger strategic decisions by appreciating where to invest in infrastructure, when to scale through platforms, and how agents and applications transform end-user experiences.

Identify the use cases and strengths of Google’s foundation models.

• Gemini
• Gemma
• Imagen
• Veo

Summary: This section highlights the unique power of Google’s foundation models. Each of these models serves specialized use cases, ranging from multimodal interactions to advanced image and video generation. Knowing their strengths enables you to position the right model for the right context.

Leaders who understand this landscape can guide organizations in selecting the right tools while streamlining adoption. The ability to match models like Gemini or Imagen with high-value use cases translates into tailored strategies that maximize return on investment.

Domain 2: Google Cloud’s gen AI offerings (35% of the exam)

Describe Google Cloud's strengths in the field of gen AI.

• 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 (responsible, secure, private, reliable, scalable).
• Recognizing the advantages of Google's comprehensive AI ecosystem (integration of gen AI across Google products and services).
• Describing the benefits of Google Cloud's open approach.
• Identifying the essential components of Google Cloud’s AI-optimized infrastructure and its benefits (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 (security, privacy, governance, open and leading first party models, pre-built and customizable solutions, agents).
• Describing how Google Cloud's AI platform democratizes AI development (low-code and no-code tools, pre-trained models, APIs).

Summary: This section helps you grasp what makes Google Cloud’s generative AI portfolio distinctive. You’ll get a complete picture of its enterprise-ready qualities, integration across Google’s ecosystem, and the benefits of open frameworks. This understanding empowers you to champion scalable and secure AI strategies.

For professionals leading organizational adoption, this knowledge provides confidence to align Google Cloud AI services with enterprise needs. Recognizing components such as TPUs and low-code capabilities translates into actionable insights on how to accelerate transformation while ensuring security and governance.

Describe how Google Cloud’s prebuilt gen AI offerings enable AI powered work.

• Recognizing the functionality, use cases, and business value of the Gemini app and Gemini Advanced (Gems).
• Recognizing the functionality, use cases, and business value of Google Agentspace (Cloud NotebookLM API, multimodal search, custom agent capabilities).
• Recognizing the functionality, use cases, and business value of Gemini for Google Workspace.

Summary: This section introduces Google Cloud’s powerful prebuilt applications that enable immediate business value. You’ll learn about the practical functions of tools like Gemini apps, Agentspace, and Workspace integrations. These tools bring generative AI to life in daily operations, increasing productivity and enhancing creativity.

As a leader, knowing these prebuilt offerings allows you to show quick wins to business teams. These tools validate AI’s potential by immediately solving challenges like productivity, research, and communication, and lay the foundation for larger innovation strategies.

Describe how Google Cloud’s gen AI offerings improve the customer experience.

• Recognizing the functionality, use cases, and business benefits of Google Cloud’s external search offerings (Vertex AI Search, Google Search).
• Recognizing the functionality, use cases, and business value of Google’s Customer Engagement Suite (Conversational Agents, Agent Assist, Conversational Insights, Google Cloud Contact Center as a Service).

Summary: This section emphasizes how generative AI can directly shape customer-facing experiences. With solutions like Vertex AI Search and Google’s Customer Engagement Suite, businesses can deliver intelligent interactions that adapt to end-users’ needs in real time.

By harnessing these tools, you can lead initiatives that elevate customer satisfaction, improve retention, and drive greater value. This knowledge helps you align business priorities with AI-driven strategies aimed at enhancing engagement.

Describe how Google Cloud empowers developers to build with AI.

• Recognizing the functionality, use cases, and business value of Vertex AI Platform (Model Garden, Vertex AI Search, AutoML).
• Recognizing the functionality, use cases, and business value of Google Cloud’s RAG offerings (prebuilt RAG with Vertex AI Search, RAG APIs).
• Recognizing the functionality, use cases, and business value of using Vertex AI Agent Builder to build custom agents.

Summary: This section places an emphasis on how developers partner with Google Cloud to accelerate innovation. You’ll learn about Vertex AI, custom agent builder tools, and retrieval-augmented generation offerings that make building AI applications accessible and efficient.

Understanding these resources enables you to empower technical teams with the tools they need. For leaders, it means positioning your organization to rapidly customize solutions, integrate services, and maintain a competitive advantage.

Define the purpose and types of tooling for gen AI agents.

• Identifying how agents use tools to interact with the external environment and achieve tasks (extensions, functions, data stores, and plugins).
• Identifying relevant Google Cloud services and pre-built AI APIs for agent tooling (Cloud Storage, databases, Cloud Functions, Cloud Run, Vertex AI, 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 Vertex AI Studio and Google AI Studio.

Summary: In this section, you’ll explore the vital role that agent tooling plays in generative AI systems. Agents equipped with extensions and integrations act as bridges between models and business workflows. Tools like Vertex AI Studio and prebuilt APIs give teams fast ways to enrich solutions.

Armed with this knowledge, you’ll be able to guide discussions about AI extensibility and scalability. Choosing the right tools means setting your organization up for ongoing innovation and adaptability.

Domain 3: Techniques to improve gen AI model output (20% of the exam)

Describe how to proactively overcome foundation model limitations.

• Identifying common limitations of foundation models (data dependency, the knowledge cutoff, bias, fairness, hallucinations, edge cases).
• Describing the Google Cloud-recommended practices to address limitations (grounding, retrieval-augmented generation, prompt engineering, fine-tuning, human in the loop).
• Recognizing Google-recommended practices for continuous monitoring and evaluation of gen AI models (automatic model upgrades, key performance indicators, security patches and updates, versioning, performance tracking, drift monitoring, Vertex AI Feature Store).

Summary: This section illustrates strategies to identify and mitigate common model shortcomings. Limitations such as bias, hallucinations, or dependency on outdated data are explained along with Google Cloud’s practical recommendations for resolution.

Leaders will be empowered with the vocabulary and tools to identify risks early and pattern proactive mitigation into project planning. Bridging continuous monitoring practices with business goals ensures long-term AI reliability aligned with enterprise expectations.

Describe prompt engineering techniques and how they drive better results.

• Defining prompt engineering and describing its significance in interacting with large language models (LLMs).
• Identifying prompting techniques and use cases (zero-shot, one-shot, few-shot, role prompting, prompt chaining).
• Identifying advanced prompting techniques and when to use them (chain-of-thought prompting, ReAct prompting).

Summary: This section introduces prompt engineering, which is the art of shaping inputs to receive meaningful AI outputs. You will learn step-by-step prompting strategies ranging from role prompting to advanced methods like chain-of-thought prompting.

With skill in prompt engineering, you can champion measurable outcomes for business challenges. This knowledge empowers you to guide teams toward improved quality, ensuring generative AI provides consistent, accurate, and innovative responses.

Identify grounding techniques and their use cases.

• Describing the concept of grounding in LLMs and differentiating between grounding with first-party enterprise data, third-party data, and world data.
• Describing how retrieval-augmented generation (RAG) can affect the generated output from your gen AI models.
• Google Cloud grounding offerings: Pre-built RAG with Vertex AI Search.
• Google Cloud grounding offerings: RAG APIs.
• Google Cloud grounding offerings: Grounding with Google Search.
• Identifying how sampling parameters and settings are used to control the behavior of gen AI models (token count, temperature, top-p, safety settings, and output length).

Summary: Grounding ensures generative AI models operate with accuracy and trusted context. You’ll explore when to ground models with enterprise-owned data versus global data, and how RAG enhances model reliability. Additionally, you’ll identify sampling methods that fine-tune control over outputs.

With this knowledge, you can direct teams to balance creativity and governance effectively. These techniques ensure AI-generated content is aligned with both technical and business objectives.

Domain 4: Business strategies for a successful gen AI solution (15% of the exam)

Describe the Google Cloud-recommended steps to successfully implement a transformational gen AI solution.

• Recognizing the different types of gen AI solutions (text generation, image generation, code generation, personalized user needs).
• Identifying the key factors that influence gen AI needs (business requirements, technical constraints).
• Describing how to choose the right gen AI solution for a specific business need.
• Identifying the steps to integrate gen AI into an organization.
• Identifying techniques to measure the impact of gen AI initiatives.

Summary: This section highlights the practical process of integrating generative AI across the business. You’ll learn how to assess needs, match solutions, and monitor progress. This ensures AI initiatives deliver measurable outcomes.

For professionals, this equips you with tools to align business direction with appropriate AI strategies. Doing so helps organize change management and creates momentum for ongoing innovation.

Define secure AI and its importance in protecting AI systems from malicious attacks and misuse.

• Explaining security throughout the ML lifecycle.
• Identifying the purpose and benefits of Google’s Secure AI Framework (SAIF).
• Recognizing Google Cloud security tools and their purpose (secure-by-design infrastructure, Identity and Access Management, Security Command Center, workload monitoring tools).

Summary: This section centers on maintaining security throughout the entire AI lifecycle. The Secure AI Framework is highlighted as an essential guide to ensuring trustworthiness and resilience.

By embracing the tools and strategies Google provides, leaders can safeguard initiatives and strengthen enterprise confidence in AI adoption. Security is positioned as an enabler of trust, ensuring AI delivers long-term business value responsibly.

Describe the importance of responsible AI in business.

• Explaining the importance of responsible AI and transparency.
• Describing privacy considerations (privacy risks, data anonymization and pseudonymization).
• Describing the implications of data quality, bias, and fairness.
• Describing the importance of accountability and explainability in AI systems.

Summary: This section introduces principles of responsible AI. Topics include privacy considerations, fairness, bias management, and the need for transparency and accountability. These are key factors influencing both compliance and ethical alignment.

As organizations scale AI, responsible adoption builds trust with customers and fosters alignment with social expectations. By mastering this, leaders can ensure AI strategies create shared value in a sustainable, ethical, and transparent way.