AWS Certified Machine Learning Engineer - Associate (MLA-C01)

The AWS Certified Machine Learning Engineer – Associate (MLA‑C01) exam evaluates a practitioner’s capability to turn business problems into reliable, cost‑effective machine learning solutions on AWS and keep them operating in production. Candidates are assessed on end‑to‑end engineering skills across four areas: preparing data for learning, developing models, deploying and orchestrating workflows, and monitoring, maintaining, and securing ML systems. Success depends on sound judgment about tradeoffs among performance, latency, cost, and maintainability, and on consistent use of AWS services that support automation, observability, and governance. Baseline competence includes practical familiarity with SageMaker features for data prep, training, tuning, and deployment; understanding of common ML algorithms and their use cases; and the ability to query, transform, and version code and artifacts. Candidates should be comfortable with CI/CD, infrastructure as code, and provisioning and monitoring compute, storage, and networking resources in the AWS Cloud. Secure design and operation—least‑privilege access, encryption, and data protection—are recurring themes alongside data integrity, bias mitigation, and compliance considerations for sensitive information. In data preparation, candidates must ingest batch and streaming data, choose storage and file formats that fit access patterns, and consolidate sources at scale. They clean and transform data, engineer and manage features, and label datasets using AWS tools. They are expected to validate quality, detect and address pre‑training bias, and prepare datasets for efficient loading into training jobs while meeting encryption, classification, anonymization, and residency requirements. In model development, candidates select viable approaches given problem framing, data characteristics, interpretability needs, and budget. They choose between built‑in algorithms, foundation models, and AI services when appropriate, and train with managed frameworks. They tune hyperparameters, control over/underfitting, apply regularization, ensemble models when needed, compress or prune to meet constraints, integrate externally built models, and manage versions for reproducibility and auditability. Performance analysis covers metric selection, baseline creation, convergence debugging, bias assessment, and comparison of shadow versus production variants. In deployment and orchestration, candidates select compute and endpoint patterns—real‑time, asynchronous, batch, serverless, edge—and container strategies, then automate provisioning with IaC. They apply autoscaling policies, configure networking, and use the SageMaker SDK and pipeline orchestrators to connect data, training, and inference stages. CI/CD pipelines implement testable, rollback‑friendly strategies (blue/green, canary, linear) and support triggers for retraining. In monitoring, maintenance, and security, candidates instrument models and data for drift and quality, set alarms and dashboards, and investigate latency or scaling issues. They right‑size resources, manage costs with tagging and analysis tools, and choose purchasing options to optimize spend. Security expectations include IAM and network controls, pipeline hardening, and continuous audit and logging. The scope excludes architecting full end‑to‑end ML strategies, broad multi‑domain ML research, extensive cross‑service integrations, and model quantization analysis.