AWS Machine Learning Engineer Associate (MLA-C01) Exam Prep

About this course

This course takes you from intermediate AWS and machine learning knowledge to exam-ready Machine Learning Engineer in 6–8 weeks, with comprehensive practice questions and in-depth explanations covering all MLA-C01 exam domains, based on real AWS exam patterns and the official exam guide. WHAT YOU’LL LEARN1. Data Preparation & Feature Engineering (20%)Data ingestion and preprocessing using Amazon S3, Glue, and AthenaFeature engineering techniques for structured and unstructured dataData validation, cleaning, and handling missing or imbalanced dataSchema evolution and data versioning strategiesFeature Store concepts and use casesChoosing batch vs streaming data pipelines2. Model Training & Experimentation (24%)Model training using Amazon SageMaker built-in algorithms and custom frameworksHyperparameter tuning with SageMaker Automatic Model TuningDistributed training and managed spot trainingExperiment tracking, metrics, and model lineageAlgorithm selection based on problem type and data characteristicsHandling overfitting, underfitting, and bias3. Model Deployment & Inference (20%)Real-time vs batch inference architectureDeploying models with SageMaker endpointsAuto scaling, A/B testing, and multi-model endpointsBlue/green and canary deployments for ML modelsLatency, throughput, and cost optimizationModel rollback and version control4. ML Workflow Automation & Orchestration (14%)End-to-end ML pipelines using SageMaker PipelinesEvent-driven workflows with Step Functions, Lambda, and EventBridgeCI/CD for ML (MLOps) best practicesAutomating retraining and inference triggersPipeline failure handling and recovery strategies</