[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cheat-sheet---en":3,"domain-info---en":3,"topic-info----en":3,"lesson-aws-certified-ai-practitioner-foundation-model-applications-fm-application-design-fm-customization-tradeoffs-en":4,"prev-aws-certified-ai-practitioner-foundation-model-applications-fm-application-design-fm-customization-tradeoffs-en":18,"next-aws-certified-ai-practitioner-foundation-model-applications-fm-application-design-fm-customization-tradeoffs-en":28},null,{"locked":5,"reason":6,"meta":7,"item":3},true,"paywall",{"title":8,"description":9,"isFree":10,"estimatedMinutes":11,"difficulty":12,"learningObjectives":13},"Customization Tradeoffs","Prompt engineering, RAG, fine-tuning, and continued pre-training form a ladder of rising cost. This lesson maps what each one changes, what problem it solves, and why you start at the cheap end.",false,18,"intermediate",[14,15,16,17],"Order the customization approaches from lowest to highest cost and complexity","Explain what each approach changes and what problem it solves","Explain why AWS advises starting with prompt engineering and RAG before fine-tuning","Match a scenario to the cheapest approach that solves it",{"locked":5,"reason":6,"meta":19,"item":3},{"title":20,"description":21,"isFree":10,"estimatedMinutes":22,"difficulty":12,"learningObjectives":23},"Vector Databases on AWS","The store that makes retrieval possible: what a vector database does, why RAG needs one, and the AWS services the exam expects you to name for holding embeddings.",16,[24,25,26,27],"Explain what a vector database stores and the search it performs","Explain why similarity search needs a purpose-built index at scale","Name the AWS services the exam lists for storing embeddings and match each to a fit","Choose a vector store based on an existing data stack",{"locked":5,"reason":6,"meta":29,"item":3},{"title":30,"description":31,"isFree":10,"estimatedMinutes":11,"difficulty":12,"learningObjectives":32},"AI Agents in Applications","When a single prompt cannot finish the job: how agents reason across multiple steps and take actions, how Amazon Bedrock Agents orchestrate that loop, and where an agent fits versus plain RAG.",[33,34,35,36,37],"Explain what an agent adds beyond a single prompt-and-response","Describe the reason, act, observe loop an agent runs","Explain how Amazon Bedrock Agents use action groups and knowledge bases","Distinguish an agent from RAG and from a plain model call","Decide when a task warrants an agent"]