Define and describe the core gen AI concepts.

Explain how various data types are used in generative AI to solve business problems, drive innovation, and gain a competitive advantage.

Explain the role of foundation models in generative AI, including their capabilities and limitations.

Describe the Google Cloud-recommended strategies for handling LLM limitations.

Describe the layers of the gen AI landscape and how you can come in at different points to address specific business needs and drive innovation.

Identify the core layers of the gen AI landscape (infrastructure, models, platforms, agents, applications)

Explain how Google Cloud’s AI-optimized infrastructure, data centers, and cloud computing platform supports generative AI development.

Describe the business factors to consider when deciding which part of the Google Cloud gen AI portfolio to use for specific applications.

Describe prompt engineering techniques and how they drive better results.

Describe the core functionality and business value of using Google’s gen AI applications.

Develop automated workflows by using tools like Gems, and NotebookLM to increase individual and team productivity.

Apply grounding techniques to generate relevant and accurate responses from language models.

Define the components of generative AI agents and how they work together.

Explain how you can combine the components of generative AI agents to build powerful solutions.

Describe how you can use Google Cloud’s gen AI products to create generative AI agents.

Explain how to effectively lead and manage organizational transformation with gen AI.