Practical AI Use Cases

Knowing what AI is only pays off when you can decide where to use it. This topic turns the vocabulary from the first topic into judgment: given a business problem, should you use machine learning at all, and if so, which technique, which application pattern, and which AWS service fit best? It maps directly to the exam objective on identifying practical use cases for AI, and it rewards clear thinking over enthusiasm for the newest model.

What This Topic Covers

• The three patterns where AI and ML add value (assisting decisions, scaling, automation) and the cost-benefit and exact-outcome tests that tell you when to say no
• Matching a use case to regression, classification, or clustering by the shape of the answer you need
• The real-world application areas the exam names: computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting, knowledge bases, and agentic AI
• The AWS managed AI services and their three layers, from pre-built APIs like Comprehend, Transcribe, Translate, Lex, Polly, and Rekognition to building your own models on SageMaker AI
• Choosing between a traditional ML model and a foundation model based on explainability, regulation, cost, and operational constraints

Why It Matters

Most exam questions in this domain are scenarios, not definitions. You are handed a situation and asked to pick the right technique, service, or class of model, and the wrong instinct (reach for AI everywhere, always pick the most powerful model) is exactly what the traps punish. Learning to read a problem for its constraints, whether it needs an exact answer, whether it must be explainable, whether you have data, tells you the answer before you weigh options. That same judgment carries straight into real projects, where choosing the right approach up front saves far more than clever modeling later.