AWS ML Engineer Associate study guide
AWS ML Engineer Associate · Work through your exam, one topic at a time.
The AWS Certified Machine Learning Engineer – Associate (MLA-C01) is a role-based credential for engineers who build, deploy, and operate ML workloads on AWS rather than for researchers who design novel algorithms. It suits data engineers, MLOps engineers, and software engineers who already work with services such as SageMaker, Glue, and Lambda and want formal recognition of production ML skills. The AWS ML Engineer Associate study guide has 4 sections and about 1,331 words.
01Data Preparation for Machine Learning
On AWS, preparation spans three layers: storage (S3 as the source of truth), catalog and query (AWS Glue Data Catalog, Athena, Lake Formation permissions), and processing (Glue ETL jobs, SageMaker Processing, SageMaker Data Wrangler, EMR for Spark at scale). Know which tool fits which constraint: Data Wrangler for interactive exploration and export to a pipeline; Glue for serverless Spark and catalog maintenance; SageMaker Processing for containerized, repeatable transforms that sit inside a pipeline step.
389 words, about 2 min02ML Model Development
SageMaker offers built-in algorithms (XGBoost, linear learner, k-means, DeepAR, BlazingText), framework containers (PyTorch, TensorFlow, scikit-learn), and custom containers. Built-ins are fastest to production and support distributed training; script mode lets you bring existing code with minimal changes.
314 words, about 2 min03Deployment and Orchestration of ML Workflows
Know the four serving patterns: real-time endpoints (persistent, low latency), serverless inference (scale to zero, cold starts, payload and timeout limits), batch transform (large datasets, no endpoint), and asynchronous inference (large payloads, long runtimes, near-real-time with S3 input/output). Multi-model endpoints host many models behind one endpoint and load them on demand; multi-container endpoints run several containers on one endpoint.
304 words, about 2 min04ML Solution Monitoring, Maintenance, and Security
SageMaker Model Monitor captures endpoint data and compares it to a baseline: data quality, model quality (requires ground-truth labels merged later), bias drift, and feature attribution drift. Traps: expecting model quality monitoring to work without labels, or forgetting that monitoring schedules need a baseline produced by a baseline job.
324 words, about 2 min