[{"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-vector-databases-on-aws-en":4,"next-aws-certified-ai-practitioner-foundation-model-applications-fm-application-design-vector-databases-on-aws-en":18,"prev-aws-certified-ai-practitioner-foundation-model-applications-fm-application-design-vector-databases-on-aws-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},"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.",false,16,"intermediate",[14,15,16,17],"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":19,"item":3},{"title":20,"description":21,"isFree":10,"estimatedMinutes":22,"difficulty":12,"learningObjectives":23},"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.",18,[24,25,26,27],"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":29,"item":3},{"title":30,"description":31,"isFree":10,"estimatedMinutes":32,"difficulty":12,"learningObjectives":33},"Retrieval Augmented Generation (RAG)","Give a model access to your private, current data without retraining it: how RAG retrieves relevant passages at query time, how Amazon Bedrock Knowledge Bases automates it, and where the boundary with fine-tuning sits.",20,[34,35,36,37,38],"Explain the problem RAG solves that a base model cannot","Describe the two phases of RAG: ingestion and retrieval at query time","Explain what Amazon Bedrock Knowledge Bases automates","Distinguish RAG from fine-tuning and from pasting everything into a long context window","Identify business applications where RAG is the right fit"]