A data team wants a managed AWS platform to build, train, and deploy custom machine learning models for forecasting permit demand—without needing an AI Practitioner-level foundation-model deep dive. Which service should they identify?
Select an answer to reveal the explanation.
Short Explanation
When the ask is build, train, and deploy custom ML models on a managed AWS platform, Amazon SageMaker is the name to spot. Cloud Practitioner level is identify-the-service, not design a foundation-model lab from scratch.
Full Explanation
Amazon SageMaker is the managed platform commonly identified for building, training, and deploying machine learning models on AWS. Cloud Practitioner coverage stays at service identification for common ML use cases, not deep foundation-model practitioner design. Networking, DDoS, and reachability tools do not replace a managed ML development and training platform.