課程名稱：Google Cloud Certified Generative AI Leader 國際認可證書課程 (1 科 Google AI)
- 簡稱：Google Generative AI Training Course |
Exam PR000309: Google Cloud Certified Generative AI Leader
Section 1: Fundamentals of gen AI
1.1 Describe core generative AI (gen AI) concepts and use cases
- 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).
- Describing the machine learning approaches (e.g., 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 (e.g., 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 (e.g., 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 (e.g., 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.
1.2 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 (e.g., 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.
1.3 Identify the core layers of the gen AI landscape and the business implications.
- Infrastructure layer
- Models layer
- Platforms layer
- Agents layer
- Applications layer
1.4 Identify the use cases and strengths of Google's foundation models.
Section 2: Google Cloud's gen AI offerings
2.1 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 (e.g., responsible, secure, private, reliable, scalable).
- 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.
- 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).
- 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
- 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., Gemini Notebook 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.
- 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).
2.4 Describe how Google Cloud empowers developers to build with AI
- 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).
- 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
- Identifying how agents use tools to interact with the external environment and achieve tasks (e.g., extensions, functions, data stores, and plugins).
- 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.
Section 3: Techniques to improve gen AI model output
3.1 Describe how to proactively overcome foundation model limitations
- Identifying common limitations of foundation models (e.g., data dependency, the knowledge cutoff, bias, fairness, hallucinations, edge cases).
- 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]).
- 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
- Defining prompt engineering and describing its significance in interacting with large language models (LLMs).
- Identifying prompting techniques and use cases (e.g., zero-shot, one-shot, few-shot, role prompting, prompt chaining).
- Identifying advanced prompting techniques and when to use them (e.g., chain-of-thought prompting, ReAct prompting).
3.3 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:
a) Pre-built RAG with Agent Search
b) RAG APIs
c) Grounding with Google Search
- 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).
Section 4: Business strategies for a successful gen AI solution
4.1 Describe the Google Cloud-recommended steps to successfully implement a transformational gen AI solution
- Recognizing the different types of gen AI solutions (e.g., text generation, image generation, code generation, personalized user needs).
- Identifying the key factors that influence gen AI needs (e.g., 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.
4.2 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 (e.g., secure-by-design infrastructure, Identity and Access Management (IAM), Security Command Center, and workload monitoring tools).
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).
- Describing the implications of data quality, bias, and fairness.
- Describing the importance of accountability and explainability in AI systems.
The course content above may change at any time without notice in order to better reflect the contents of examination.
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