AWS publishes exactly what the MLA-C02 tests and how much each part is worth. Here it is — every domain, its weight, the topics inside it, and the lessons that cover them.
Domain 1
Data Preparation for ML and AI
Collecting and storing data for ML and AI, transforming it and engineering features, and validating data quality and managing bias — for traditional ML and foundation models alike.
3 topics in this domain
- Collect and store data
- Perform data transformation, feature engineering, and pre-processing
- Validate data quality and manage bias
Domain 2
ML Model and Foundation Model (FM) Development
Choosing modeling approaches — traditional ML, managed AI services, foundation models and RAG — then training, fine-tuning and customizing models, and evaluating ML and GenAI performance.
3 topics in this domain
- Choose appropriate modeling approaches for ML and AI solutions
- Train, fine-tune, and customize models for ML and AI solutions
- Analyze and evaluate the performance of ML and AI systems
Domain 3
Deployment and Orchestration of ML and AI Workflows
Selecting deployment infrastructure for ML models, foundation models and agents, provisioning and scaling the resources behind them, and automating MLOps with CI/CD pipelines.
3 topics in this domain
- Manage deployment infrastructure for ML and AI model types
- Provision and configure resources for ML and AI workloads based on existing architecture and requirements
- Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads
Domain 4
Operating, Monitoring, and Securing ML and AI Solutions
Monitoring models, agents and data in production, optimizing infrastructure and inference cost, and securing ML and AI workloads and endpoints.
3 topics in this domain
- Monitor ML and AI model inference and performance
- Optimize and manage ML and AI infrastructure costs and performance
- Secure ML and AI workloads and model endpoints