Domain 2 · 26% of Exam

ML Model Development

Domain 2 covers selecting ML approaches, training models, tuning hyperparameters, analyzing model performance, and managing versions.

What You'll Be Tested On

  • Selecting model types and algorithms for classification, regression, clustering, forecasting, and anomaly detection
  • Configuring SageMaker training jobs, built-in algorithms, custom containers, and distributed training
  • Evaluating models with appropriate metrics and diagnosing overfitting or underfitting
  • Running hyperparameter tuning jobs and selecting objective metrics
  • Registering and versioning model candidates for later deployment

Key AWS Services in This Domain

SMSageMakerTRModel TrainingALML AlgorithmsEVModel EvaluationHTHyperparameter TuningBRBedrockCOML Cost Optimization

Exam Tips for Domain 2

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Match the metric to the business problem: precision/recall for imbalanced classification, RMSE for regression, and AUC for ranking quality.

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If training is expensive and interruptible, managed spot training is often the cost-aware answer.

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Use hyperparameter tuning when the model approach is chosen but performance needs systematic improvement.

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Model versioning and approval belong in the model registry, not in ad hoc file names.

Practice Domain 2 Questions

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Other MLA-C01 Domains

Domain 1Data Preparation for Machine Learning28%Domain 3Deployment and Orchestration of ML Workflows22%Domain 4ML Solution Monitoring, Maintenance, and Security24%
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