[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cheat-sheet---en":3,"domain-info---en":3,"topic-info----en":3,"next-aws-certified-ai-practitioner-ai-ml-fundamentals-practical-ai-use-cases-real-world-ai-applications-en":4,"lesson-aws-certified-ai-practitioner-ai-ml-fundamentals-practical-ai-use-cases-real-world-ai-applications-en":18,"prev-aws-certified-ai-practitioner-ai-ml-fundamentals-practical-ai-use-cases-real-world-ai-applications-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},"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.",false,18,"beginner",[14,15,16,17],"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":19,"item":3},{"title":20,"description":21,"isFree":10,"estimatedMinutes":22,"difficulty":12,"learningObjectives":23},"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.",17,[24,25,26,27],"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":29,"item":3},{"title":30,"description":31,"isFree":10,"estimatedMinutes":32,"difficulty":12,"learningObjectives":33},"Regression, Classification, and Clustering","The three workhorse ML techniques and how to pick the right one for a use case, using the shape of the answer you need: a number, a category, or a set of natural groups.",16,[34,35,36,37],"Match a use case to regression, classification, or clustering by the shape of the answer it needs","Distinguish regression from classification even when the same subject can be framed either way","Explain when clustering fits: no labels exist and you want to discover groups","Avoid the common traps of confusing a number with a category and a known label with a discovered group"]