Fundamentals of GenAI

Before a generative AI model can read your text, the text is broken into tokens. Before it can search your documents, they are cut into chunks and turned into embeddings, which are lists of numbers called vectors. This lesson explains all four ideas and how they work together, because almost every…

Most generative AI today runs on a handful of model designs. Transformer-based large language models produce text, multimodal models handle more than one kind of input or output, and diffusion models produce images. This lesson explains how each works at a high level, what a foundation model is…

Generative AI earns its place where a business needs new content produced quickly and at scale, or needs to turn a large amount of information into something a person can use. This lesson walks through the common use cases the exam expects you to recognize, and how to tell a genuine generative AI…

A foundation model goes through a recognizable lifecycle, from choosing its data to learning from users after launch. Most organizations only work in the later stages, because they start from a model someone else has already pre-trained. This lesson walks through each stage and shows which ones a…

Most foundation models are paid for by the token, so the length of what you send and what the model sends back drives both the bill and the wait. This lesson explains how token-based pricing works, why input and output are priced separately, and the levers a team can pull to lower cost and speed…

A foundation model only knows two things when it answers: what it learned in training, and what is in the request in front of it. Context engineering is the practice of deciding what goes into that request, so the model has the right information, in the right amount, at the right time. This lesson…

Domain 1 introduced agentic AI as AI that plans and acts toward a goal. This lesson opens up the parts that make an agent work: tools, memory and orchestration. It then covers how several agents can work as a team, and how the Model Context Protocol (MCP) gives agents a standard way to connect to…

Generative AI earns its place in a business when its strengths match the problem. Four strengths come up again and again: adaptability, responsiveness, conversation, and the ability to generate new content. This lesson explains each one in plain terms and shows how to match it to a business need…

Generative AI has four weaknesses the exam expects you to recognize from a short scenario: hallucinations, inaccuracy, nondeterminism and limited interpretability. Knowing which one a scenario describes points you to the right fix, whether that's grounding answers in your own data, adjusting…

There's no single best generative AI model, only the best fit for a job. Choosing one means weighing the model type against the task, then performance and capabilities, then constraints such as cost, latency, model complexity and compliance. The exam gives you a scenario with one deciding…

A generative AI project succeeds when it moves a business number, not when its model scores well in a lab. This lesson covers the metrics businesses use to judge generative AI: return on investment (ROI), efficiency, conversion rate, average revenue per user (ARPU), customer lifetime value (CLV)…

AWS offers several services for building generative AI (GenAI) applications, and they sit at different levels. Some give you models, some help you build and run agents, and some are finished assistants that people just use. Exam questions usually describe what a team wants to build and how much it…

Managed generative AI (GenAI) services such as Amazon Bedrock change what it takes to build an AI feature. Instead of buying hardware, hosting models and hiring specialists, a team calls an API and pays for what it uses. The exam expects you to name these advantages and match each one to a…

Many companies hesitate to use generative AI (GenAI) because they worry about where their data goes, whether they can meet regulations, and what the model might say to customers. Running GenAI on AWS infrastructure answers those concerns with four benefits the exam names: security, compliance…

Generative AI (GenAI) on AWS can be paid for in several ways, and each one trades cost against speed, availability or commitment. The exam gives a workload, such as a nightly batch job, a steady high-volume app or an unpredictable prototype, and asks for the most cost-effective option that still…