[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cheat-sheet---en":3,"domain-info---en":3,"topic-info----en":3,"lesson-aws-certified-ai-practitioner-genai-fundamentals-genai-core-concepts-genai-use-cases-en":4,"next-aws-certified-ai-practitioner-genai-fundamentals-genai-core-concepts-genai-use-cases-en":18,"prev-aws-certified-ai-practitioner-genai-fundamentals-genai-core-concepts-genai-use-cases-en":29},null,{"locked":5,"reason":6,"meta":7,"item":3},true,"paywall",{"title":8,"description":9,"isFree":10,"estimatedMinutes":11,"difficulty":12,"learningObjectives":13},"Generative AI Use Cases","Work through the use cases the exam names, grouped by the capability behind them: generating new content, transforming existing content, holding a conversation, and finding what matters.",false,16,"beginner",[14,15,16,17],"Identify the generative AI use cases named in the exam guide and the capability each one draws on","Distinguish an AI assistant from a customer service agent and search from recommendation","Explain why summarization and translation carry lower risk than open-ended generation","Judge from a scenario whether generative AI is the right fit or traditional ML is a better answer",{"locked":5,"reason":6,"meta":19,"item":3},{"title":20,"description":21,"isFree":10,"estimatedMinutes":22,"difficulty":23,"learningObjectives":24},"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,"intermediate",[25,26,27,28],"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":30,"item":3},{"title":31,"description":32,"isFree":10,"estimatedMinutes":22,"difficulty":23,"learningObjectives":33},"Multimodal and Diffusion Models","Understand how generative AI works beyond text: what makes a model multimodal, how diffusion models build images by removing noise step by step, and how diffusion differs from GANs and from transformer language models.",[34,35,36,37],"Define a multimodal model and distinguish it from a multi-model architecture","Explain forward and reverse diffusion and how a text prompt steers image generation","Describe why diffusion models work in latent space rather than on raw pixels","Compare diffusion models with GANs and with transformer-based language models"]