This guide covers the official exam objectives for the AWS Certified Machine Learning Engineer Associate certification, organized into focused chapters that map to data preparation, model development, monitoring/security, and deployment/orchestration domains.
This guide works best as a loop: read a chapter, test yourself with practice questions, look up unfamiliar terms in the glossary, then move to the next chapter.
16 chapters covering every exam objective. Each chapter includes key concepts, exam tips, common traps, comparison tables, and a 5-question quiz at the end.
Start Chapter 1Free timed and untimed practice with instant feedback and full explanations. Pick 10–120 questions per session. Filter by domain to drill your weak areas.
Go to practice testEvery MLA-C01term defined and searchable. Use it when a chapter mentions a concept you haven't seen before or want a quick refresher on.
Browse glossaryExam blueprint, domain weights, passing score, duration, cost, and registration links. Start here if you're new to this certification.
View exam guideIntroduction to Machine Learning on AWS
Objective 1.1 · Identify the components of the ML pipeline on AWS
Data Preparation Foundations: S3, Glue, and Data Wrangling
Objective 1.2 · Prepare data for ML using AWS services
Data Transformation and Feature Engineering with SageMaker
Objective 1.3 · Perform feature engineering and data transformation for ML
Data Labeling and Quality Assurance with SageMaker Ground Truth
Objective 1.4 · Implement data labeling strategies and quality checks
Model Selection and Training Frameworks on SageMaker
Objective 2.1 · Select and train machine learning models using SageMaker built-in algorithms and frameworks
Hyperparameter Tuning and Model Optimization
Objective 2.2 · Optimize model performance using automatic model tuning and hyperparameter optimization
Model Evaluation and Validation Techniques
Objective 2.3 · Evaluate and validate model performance using appropriate metrics and cross-validation
Building and Automating ML Pipelines with SageMaker Pipelines
Objective 2.4 · Create and manage end-to-end ML pipelines using SageMaker Pipelines
Model Registry and Versioning with SageMaker
Objective 2.5 · Manage model versions and lineage using SageMaker Model Registry
Monitoring Model Performance and Drift Detection
Objective 3.1 · Monitor ML models for performance degradation and data drift
Logging, Tracing, and Auditability for ML Workloads
Objective 3.2 · Implement logging and tracing for ML models and pipelines
Security, IAM, and Encryption for ML Resources
Objective 3.3 · Secure ML resources using IAM policies, encryption, and network controls
Model Deployment Strategies: Real-Time, Batch, and Serverless Inference
Objective 4.1 · Deploy ML models using SageMaker endpoints, batch transform, and serverless inference
A/B Testing and Canary Deployments for ML Models
Objective 4.2 · Implement A/B testing and gradual deployment strategies for ML models
CI/CD for Machine Learning: Automating Model Deployment
Objective 4.3 · Set up CI/CD pipelines for ML using AWS CodePipeline, CodeBuild, and SageMaker
Cost Optimization and Scaling for ML Workloads
Objective 4.4 · Optimize cost and scale ML inference and training workloads on AWS
Free MLA-C01 practice questions with full explanations. Test what you learn chapter by chapter.
MLA-C01 Practice Questions