AWS · Machine Learning
AWS Machine Learning (MLS-C01) trap evaluation
5 trap types across 200 Machine Learning questions. Know which ones cost you points — and train until they don't.
Choosing the right SageMaker service for the wrong scenario
MLS-C01 is calibrated to distinguish SageMaker capabilities from each other, not to test whether you know SageMaker exists. Autopilot, built-in algorithms, custom training containers, and pre-trained models each fit different constraints around control, customization, and operational overhead. A common failure is reaching for SageMaker where a managed AI service (Rekognition, Comprehend, Forecast) fits the described use case more precisely, or choosing a built-in algorithm when the scenario specifies a dataset type or access pattern that points to a custom container. Deployment architecture is a second failure cluster: batch transform versus real-time endpoint selection maps to the inference pattern described, and confusing them reflects reading the question without identifying the inference constraint.
See how these traps overlap with question patterns on the AWS Machine Learning Pattern Recognition page, or review the full AWS Machine Learning Exam Guide.
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