[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cheat-sheet---en":3,"domain-info---en":3,"topic-info----en":3,"next-aws-certified-ai-practitioner-genai-fundamentals-genai-core-concepts-token-based-pricing-en":4,"prev-aws-certified-ai-practitioner-genai-fundamentals-genai-core-concepts-token-based-pricing-en":18,"lesson-aws-certified-ai-practitioner-genai-fundamentals-genai-core-concepts-token-based-pricing-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},"Context Engineering","Treat the context window as a budget you allocate rather than a box you fill: what competes for the space, why more context can make answers worse, and the strategies that decide what earns a place in the prompt.",false,20,"intermediate",[14,15,16,17],"Define the context window and identify everything that consumes it","Explain the difference between the hard context limit and quality degradation within it","Distinguish context engineering from prompt engineering","Compare the strategies for keeping a context window within budget",{"locked":5,"reason":6,"meta":19,"item":3},{"title":20,"description":21,"isFree":10,"estimatedMinutes":22,"difficulty":12,"learningObjectives":23},"The Foundation Model Lifecycle","Walk the seven stages the exam names, from data selection to feedback, and see why model selection replaces training as the decision that matters most when you build on a foundation model.",18,[24,25,26,27],"List the stages of the FM lifecycle named in the exam guide and describe what happens in each","Distinguish pre-training from fine-tuning by data, cost, and who performs it","Explain why model selection carries more weight in the FM lifecycle than in a traditional ML pipeline","Describe how the feedback stage closes the loop and what it feeds",{"locked":5,"reason":6,"meta":29,"item":3},{"title":30,"description":31,"isFree":10,"estimatedMinutes":11,"difficulty":12,"learningObjectives":32},"Token-Based Pricing","Learn how generative AI bills, why output tokens cost more than input tokens, and how on-demand, batch, Provisioned Throughput, and prompt caching change the cost of the same workload.",[33,34,35,36],"Explain how token-based pricing works and why input and output tokens are priced differently","Estimate the monthly cost of a workload from its token volumes","Compare on-demand, batch, and Provisioned Throughput and identify which fits a given scenario","Describe how prompt caching reduces cost and what its limitations are"]