AWS Machine Learning Engineer Associate MLA-C01 — Study Guide
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This guide covers each MLA-C01 domain in depth. Pick a domain to go deep, or work through them in order.
The domains
- Data Engineering and Feature Engineering — Data engineering for ML is about producing reproducible, auditable inputs to model training and inference while minimizing leakage and operational
- Model Training and Hyperparameter Optimization — Model training in Amazon SageMaker is an orchestrated process that combines containerized training code, compute resources, persistent storage, and
- Model Evaluation and Selection — Accurate model evaluation begins with selecting metrics that match the business objective and the class / label characteristics. For binary
- Model Deployment and Inference — Serverless Inference removes instance management and is configured at the endpoint level with a ServerlessConfig that specifies MemorySizeInMB and
- MLOps and Model Lifecycle Management — Model lifecycle management in AWS centers on treating models as versioned artifacts with audited lineage, automated promotion gates, and reproducible
- Model Monitoring and Observability — Baselines are typically generated from a representative snapshot of training (and validation) data using descriptive statistics and constraint files.
- Security, Governance and Compliance — For ephemeral developer access and human approvals, prefer IAM roles assumed via AWS STS with short-lived credentials instead of long-lived IAM user
- Generative AI and Foundation Models — Foundation models are large pre-trained networks—typically transformer-based—that provide general-purpose language, vision, or multimodal
- Cost Optimization for ML Workloads — Operational techniques translate those levers into practice. Use managed Spot training with checkpointing to cut training compute costs by up to 70%
- Computer Vision, NLP and Specialized ML — Model-building choices map to specialized AWS services: Amazon SageMaker offers built-in algorithms (XGBoost, Linear Learner, Random Cut Forest) and
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