Renewed my Associate Cloud Engineer Certification through Google Skills

Google now allows eligible certification holders to extend their credentials for 12 months by earning specific skill badges and completing courses through Google Skills instead of taking the proctored exam.

I availed this opportunity to renew my Associate Cloud Engineer Certification till next year by completing courses and acquiring skill badges through Google Skills.

You can choose any of the following options to count toward renewal of eligible Google Certifications:

• 2 skill badges
• 1 skill badge & 2 courses
• 4 courses

These activities must be completed within the 12 months prior to certification expiration

The Courses and Labs are thoughtfully designed and regularly updated to stay relevant to the workplace. The AI related modules were particularly useful to me.

I compiled a glossary of Google Cloud & AI terms from the material -

Agent Platform is a comprehensive machine learning development platform that provides both predictive and generative AI capabilities. It allows you to train, evaluate, and deploy predictive machine learning models for forecasting purposes. Additionally, you can utilize the platform to discover, tune, and serve generative AI models to produce content.

Agent Studio lets you quickly test and customize generative AI models so you can leverage their capabilities in your applications. It provides a variety of tools and resources, including an intuitive user interface (UI), that make it easy to start with generative AI, even if you don't have an extensive background in machine learning.

AlloyDB is Google Cloud's scalable and performant PostgreSQL-compatible database, to store and search by a special kind of vector data called vector embeddings. 

Context caching lets you to store frequently used input tokens in a dedicated cache and reference them for subsequent requests, eliminating the need to repeatedly pass the same set of tokens to a model.

Gemini is a family of powerful generative AI models developed by Google DeepMind, capable of understanding and generating various forms of content, including text, code, images, audio, and video.

The Gemini API in Agent Platform provides a unified interface for interacting with Gemini models. This allows developers to easily integrate these powerful AI capabilities into their applications. 

Gemini Enterprise Agent Platform is Google Cloud's fully-managed, unified AI development platform for building and using generative AI. 

Gemini Flash is a multimodal foundation model optimized for speed and efficiency.

Gemini Pro is a multimodal foundation model designed for complex reasoning.

Google Gen AI SDK provides a unified interface to Google's generative AI API services. This SDK simplifies the process of integrating generative AI capabilities into applications and services, enabling developers to leverage Google's advanced AI models for various tasks

Grounding is the ability to connect model output to verifiable sources of information.

MCP Toolbox is a middleware server that exposes database operations as a set of tools. 

Prompt design is the process of creating prompts that elicit the desired response from language models. Writing well structured prompts can be an essential part of ensuring accurate, high quality responses from a language model. The iterative process of repeatedly updating prompts and assessing the model's responses is sometimes called prompt engineering.

Retrieval Augmented Generation (RAG) is the pattern of retrieving some non-public data and using that data to augment your prompt sent to the LLM. RAG allows the LLM to generate more accurate responses based on the data included in the prompt.

System instructions allow you to steer the behavior of the model. By setting the system instruction, you are giving the model additional context to understand the task, provide more customized responses, and adhere to guidelines over the user interaction.

Vector embeddings can be retrieved using a semantic search, which allows retrieval of the available data that is the best match for a user's natural language query. The retrieved data is then passed to the LLM in the prompt.