[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cheat-sheet---en":3,"domain-info---en":3,"topic-info----en":3,"prev-aws-certified-ai-practitioner-ai-ml-fundamentals-practical-ai-use-cases-aws-managed-ai-services-en":4,"lesson-aws-certified-ai-practitioner-ai-ml-fundamentals-practical-ai-use-cases-aws-managed-ai-services-en":18,"next-aws-certified-ai-practitioner-ai-ml-fundamentals-practical-ai-use-cases-aws-managed-ai-services-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},"Real-World AI Applications","The application areas the exam expects you to recognize on sight: computer vision, NLP, speech, recommendation systems, fraud detection, forecasting, knowledge bases, and agentic AI, each tied to the problem it solves.",false,17,"beginner",[14,15,16,17],"Recognize the major real-world AI application areas from a described scenario","Connect each application to the underlying technique and data it relies on","Distinguish computer vision, NLP, and speech recognition when a scenario blends them","Identify recommendation, fraud detection, forecasting, knowledge bases, and agentic AI by their signature use cases",{"locked":5,"reason":6,"meta":19,"item":3},{"title":20,"description":21,"isFree":10,"estimatedMinutes":22,"difficulty":12,"learningObjectives":23},"AWS Managed AI Services","The three layers of AWS AI/ML services and what each pre-built service does, so you can match a use case to the right managed service without building a model yourself.",18,[24,25,26,27],"Describe the three layers of AWS AI/ML services and when to reach for each","Match a use case to the correct pre-built AWS AI service by its capability","Explain the capabilities of the exam-named services: SageMaker AI, Transcribe, Translate, Comprehend, Lex, and Polly","Recognize the trade-off between ready-to-use AI services and building custom models on SageMaker",{"locked":5,"reason":6,"meta":29,"item":3},{"title":30,"description":31,"isFree":10,"estimatedMinutes":11,"difficulty":32,"learningObjectives":33},"Traditional ML vs Foundation Models","How to choose between a task-specific traditional ML model and a large general-purpose foundation model, driven by explainability, regulation, cost, and operational constraints, not just capability.","intermediate",[34,35,36,37],"Contrast traditional ML models and foundation models on scope, data needs, and how they are built","Choose between them based on explainability and regulatory requirements","Weigh operational constraints such as cost, latency, and control in the decision","Reject the assumption that the newer, more powerful foundation model is always the better choice"]