[{"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-multimodal-and-diffusion-models-en":4,"prev-aws-certified-ai-practitioner-genai-fundamentals-genai-core-concepts-multimodal-and-diffusion-models-en":18,"next-aws-certified-ai-practitioner-genai-fundamentals-genai-core-concepts-multimodal-and-diffusion-models-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},"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.",false,18,"intermediate",[14,15,16,17],"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",{"locked":5,"reason":6,"meta":19,"item":3},{"title":20,"description":21,"isFree":10,"estimatedMinutes":22,"difficulty":23,"learningObjectives":24},"Tokens, Chunking, and Embeddings","Follow a document from raw text to a searchable vector index: how text becomes tokens, why long documents get split into chunks, and how embeddings turn meaning into numbers you can compare.",20,"beginner",[25,26,27,28],"Define a token and explain why a token is not the same as a word","Explain why documents are chunked and compare the chunking strategies Amazon Bedrock supports","Describe what an embedding is and how vector similarity finds related content","Trace a document through the chunk, embed, and index pipeline that powers retrieval",{"locked":5,"reason":6,"meta":30,"item":3},{"title":31,"description":32,"isFree":10,"estimatedMinutes":33,"difficulty":23,"learningObjectives":34},"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.",16,[35,36,37,38],"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"]