AWS Machine Learning (MLS-C01) pattern recognition

SageMaker capability selection and ML pipeline design are the recurring question structures

Feature engineering and data preparation questions test which combination of SageMaker Processing, Glue, or Lambda handles the described transformation scale and trigger pattern. Model training questions focus on algorithm selection, distributed training configuration, and hyperparameter tuning scope. Deployment architecture questions repeat across real-time endpoint, multi-model endpoint, and batch transform variants, with the inference pattern as the deciding variable. MLOps questions test pipeline orchestration (SageMaker Pipelines vs Step Functions for ML workflows), model monitoring configuration, and the retraining trigger architecture. Across all of these, the exam provides enough scenario detail to resolve the choice without guessing, and the underlying reading pattern is consistent: extract the constraint, eliminate two distractors, then distinguish the remaining options by reading the scenario closely.