Google Cloud Generative AI Leader Practice Exam

Google Cloud Generative AI Leader Practice Exam

 

About Google Cloud Generative AI Leader Exam

Google Cloud Generative AI Leader is someone who understands how generative AI can reshape the way organizations work and innovate. These professionals grasp the business potential of Google Cloud’s generative AI tools and know how Google’s AI-first vision enables responsible, forward-thinking adoption. They play a key role in guiding AI-powered initiatives, spotting opportunities across industries, and helping businesses accelerate transformation using Google Cloud’s enterprise-ready solutions.

This certification is open to any professional, regardless of whether you have hands-on technical experience. It’s designed to validate business-level knowledge of generative AI and its applications.

What does the Exam cover?

The exam evaluates your understanding of:

• Generative AI fundamentals
• Google Cloud’s gen AI products and services
• Best practices for improving gen AI model outputs
• Business strategies for implementing effective gen AI solutions

Who should take the exam?

A Google Cloud Certified Generative AI Leader is a forward-thinking professional who understands how generative AI (gen AI) can drive business transformation. They hold business-level knowledge of Google Cloud’s gen AI products and services and recognize how Google’s AI-first strategy enables responsible and innovative adoption.

These leaders bridge the gap between technical and non-technical teams, fostering collaboration and guiding gen AI–driven initiatives. They are skilled at spotting opportunities and potential use cases across industries and business functions, applying Google Cloud’s enterprise-ready solutions to accelerate innovation.

Skills Required

• To prepare for the Generative AI Leader certification, candidates should have the ability to:
• Understand the fundamentals of generative AI and its business applications.
• Identify and evaluate opportunities for gen AI adoption across industries and business functions.
• Engage in effective communication with both technical and non-technical stakeholders.
• Develop strategic approaches to AI-driven initiatives within an organization.
• Evaluate Google Cloud’s gen AI products and solutions for enterprise use.
• Assess ethical, responsible, and regulatory considerations in AI adoption.
• Influence and guide AI-powered innovation without necessarily implementing technical solutions.

Knowledge Gained

• By completing this certification, candidates will gain:
• A strong understanding of generative AI concepts, capabilities, and limitations.
• Insight into Google Cloud’s generative AI offerings and how they support business transformation.
• Strategies for optimizing AI outputs to deliver meaningful business value.
• Knowledge of best practices for responsible and ethical AI adoption.
• Ability to identify potential use cases for generative AI across different business domains.
• Skills to collaborate across teams and drive AI initiatives that align with organizational goals.
• A conceptual understanding of AI technologies and workflows sufficient to guide informed business decisions.

Generative AI Leader Course Outline 

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

1.1 Describe core generative AI (gen AI) concepts and use cases
Considerations include:

• Defining key gen AI concepts such as artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, and large language models.
• Explaining machine learning approaches including supervised, unsupervised, and reinforcement learning.
• Identifying stages in the machine learning lifecycle: data ingestion, data preparation, model training, model deployment, and model management; including the Google Cloud tools relevant to each stage.
• Determining how to select the appropriate foundation model for a business use case, considering factors like modality, context window, security, availability, reliability, cost, performance, fine-tuning, and customization.
• Recognizing business applications of gen AI for tasks such as creation, summarization, discovery, and automation, including text, image, code, video generation, data analysis, and personalized user experiences.
• Understanding the role of different data types in gen AI and their business implications.
• Explaining the importance of data quality and accessibility, considering completeness, consistency, relevance, availability, cost, and format.
• Differentiating between structured and unstructured data with real-world examples.
• Differentiating between labeled and unlabeled data.

1.2 Describe how various data types are used in gen AI and their business implications
Considerations include:

• Understanding the characteristics and significance of data quality and accessibility in AI.
• Differentiating between structured and unstructured data, with practical examples.
• Differentiating between labeled and unlabeled data.

1.3 Identify the core layers of the gen AI landscape and their business implications
Considerations include:

• Infrastructure
• Models
• Platforms
• Agents
• Applications

1.4 Identify the use cases and strengths of Google’s foundation models
Considerations include:

• Gemini
• Gemma
• Imagen
• Veo

Domain 2: Google Cloud’s Generative AI Offerings (~35% of the exam)

2.1 Describe Google Cloud’s strengths in generative AI
Considerations include:

• Understanding how Google’s AI-first approach and commitment to innovation result in advanced gen AI solutions.
• Recognizing Google Cloud as an enterprise-ready AI platform: responsible, secure, private, reliable, and scalable.
• Identifying advantages of Google’s integrated AI ecosystem across products and services.
• Understanding Google Cloud’s open approach and ecosystem.
• Knowing the key components of Google Cloud’s AI-optimized infrastructure, including hypercomputers, custom TPUs, GPUs, data centers, and cloud computing benefits.
• Explaining how Google Cloud gives users control over their data, including security, privacy, governance, and access to pre-built and customizable models.
• Understanding how Google Cloud democratizes AI development through low-code/no-code tools, pre-trained models, and APIs.

2.2 Describe Google Cloud’s prebuilt gen AI offerings
Considerations include:

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

2.3 Describe how Google Cloud’s gen AI offerings improve the customer experience
Considerations include:

• Understanding external search offerings such as Vertex AI Search and Google Search.
• Recognizing the value of the Customer Engagement Suite, including Conversational Agents, Agent Assist, Conversational Insights, and Contact Center as a Service.

2.4 Describe how Google Cloud empowers developers to build with AI
Considerations include:

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

2.5 Define the purpose and types of tooling for gen AI agents
Considerations include:

• Identifying how agents leverage tools to interact with the external environment (extensions, functions, data stores, plugins).
• Understanding relevant Google Cloud services and AI APIs for agent tooling, including 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, and Google Cloud API Library.
• Determining when to use Vertex AI Studio versus Google AI Studio.

Domain 3: Techniques to Improve Generative AI Model Output (~20% of the exam)

3.1 Describe how to proactively overcome foundation model limitations
Considerations include:

• Recognizing common foundation model limitations such as data dependency, knowledge cutoff, bias, fairness issues, hallucinations, and edge cases.
• Applying Google Cloud-recommended practices to mitigate limitations, including grounding, retrieval-augmented generation (RAG), prompt engineering, fine-tuning, and human-in-the-loop (HITL).
• Implementing continuous monitoring and evaluation of gen AI models using recommended practices, including automatic upgrades, KPIs, security updates, versioning, performance tracking, drift monitoring, and Vertex AI Feature Store.

3.2 Describe prompt engineering techniques and their impact
Considerations include:

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

3.3 Identify grounding techniques and their use cases
Considerations include:

• Understanding grounding in LLMs and differences between first-party enterprise data, third-party data, and world data.
• Understanding how retrieval-augmented generation (RAG) influences gen AI output.
• Google Cloud grounding offerings:
• Pre-built RAG with Vertex AI Search
• RAG APIs
• Grounding with Google Search
• Using sampling parameters to control gen AI behavior, including token count, temperature, top-p (nucleus sampling), safety settings, and output length.

Domain 4: Business Strategies for a Successful Generative AI Solution (~15% of the exam)

4.1 Describe Google Cloud-recommended steps to implement a successful gen AI solution
Considerations include:

• Recognizing types of gen AI solutions, including text, image, code generation, and personalized experiences.
• Identifying factors influencing gen AI needs, such as business requirements and technical constraints.
• Selecting the appropriate gen AI solution for a business need.
• Planning the integration of gen AI into an organization.
• Measuring the impact of gen AI initiatives.

4.2 Define secure AI and its importance
Considerations include:

• Applying security practices throughout the ML lifecycle.
• Understanding Google’s Secure AI Framework (SAIF) and its benefits.
• Recognizing Google Cloud security tools, including secure-by-design infrastructure, Identity and Access Management (IAM), Security Command Center, and workload monitoring.

4.3 Describe the importance of responsible AI in business
Considerations include:

• Understanding the importance of responsible AI and transparency.
• Addressing privacy concerns, including risks, anonymization, and pseudonymization.
• Recognizing the implications of data quality, bias, and fairness.
• Understanding accountability and explainability in AI systems.