[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"lesson-aws-certified-ai-practitioner-foundation-model-applications-en":3,"cheat-sheet---en":3,"topic-info----en":3,"prev-aws-certified-ai-practitioner-foundation-model-applications-en":3,"next-aws-certified-ai-practitioner-foundation-model-applications-en":3,"domain-info-aws-certified-ai-practitioner-foundation-model-applications-en":4},null,{"meta":5,"body":8},{"title":6,"description":7},"Applications of Foundation Models","The largest exam domain. Choosing a foundation model, RAG and vector databases, prompt engineering, fine-tuning, and how to evaluate what you built.",{"type":9,"value":10,"toc":58},"minimark",[11,15,18,23,48,52,55],[12,13,14],"p",{},"Knowing what a foundation model is does not tell you how to build something useful with one. This is where the course turns from concepts to construction: you have a model, a business problem, and a set of choices about how to connect the two. Get those choices wrong and you overpay, leak private data into a prompt, or ship a system nobody can trust.",[12,16,17],{},"This is the heaviest domain on the exam, and it rewards judgment over memorization. It walks the full path of an application: pick the right model, ground it in your own data with RAG, shape its behavior with prompts, adapt it through fine-tuning when prompts are not enough, and then measure whether the result actually works.",[19,20,22],"h2",{"id":21},"what-this-domain-covers","What This Domain Covers",[24,25,26,30,33,36,39,42,45],"ul",{},[27,28,29],"li",{},"choosing a foundation model and tuning inference parameters like temperature and top-p",[27,31,32],{},"Retrieval Augmented Generation (RAG) and the vector databases on AWS that make it work",[27,34,35],{},"the customization spectrum from prompting to fine-tuning, and the tradeoffs at each step",[27,37,38],{},"AI agents inside applications, and where they fit alongside RAG",[27,40,41],{},"prompt engineering: anatomy, techniques, best practices, and prompt attacks like injection",[27,43,44],{},"how foundation models are trained, the main fine-tuning methods, and preparing data for them",[27,46,47],{},"evaluating models with the right metrics, plus how to assess RAG and agent applications against business goals",[19,49,51],{"id":50},"why-it-matters","Why It Matters",[12,53,54],{},"At 28 percent of the exam, this domain carries more weight than any other, and its questions are scenario-based rather than definitional. You will be handed a situation, a constraint, and four plausible options, then asked which approach fits. That is exactly the skill this domain builds: when to reach for RAG instead of fine-tuning, when a better prompt beats a bigger model, and how to tell whether what you shipped is good enough.",[12,56,57],{},"The payoff outlasts the exam. These are the same decisions real teams make when they put a foundation model into production, and getting them right is the difference between a demo and a product people can rely on.",{"title":59,"searchDepth":60,"depth":60,"links":61},"",3,[62,64],{"id":21,"depth":63,"text":22},2,{"id":50,"depth":63,"text":51}]