What Is the AWS MLA-C01?

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) validates the ability to build, deploy, and operationalize machine learning solutions on AWS. Unlike the foundational AI Practitioner exam, MLA-C01 is hands-on and engineering-focused: data preparation pipelines, feature engineering, model training and tuning, deployment patterns, CI/CD for ML, monitoring, and security. It is ideal for data scientists, ML engineers, and developers productionizing ML workloads with Amazon SageMaker.

Data Preparation for ML

2–3 weeks

Ingest data with S3, Kinesis, Glue, and AWS Lake Formation

SageMaker Data Wrangler — transforms, joins, quality checks

Feature engineering — encoding, scaling, binning, imputation

SageMaker Feature Store — online vs offline stores

Handle missing data, outliers, and class imbalance (SMOTE)

Data formats — Parquet, RecordIO, CSV; partitioning

Data labeling with SageMaker Ground Truth

Detect bias early with SageMaker Clarify

Model Development & Training

3–4 weeks

SageMaker built-in algorithms (XGBoost, Linear Learner, etc.)

Bring-your-own script and BYO container training

Automatic Model Tuning (hyperparameter optimization)

Distributed training — data and model parallelism

Spot training and managed warm pools for cost savings

Evaluation metrics and overfitting/underfitting diagnosis

SageMaker Autopilot (AutoML) and JumpStart foundation models

Experiment tracking with SageMaker Experiments

Deployment & Operationalization

3 weeks

Real-time, Serverless, Asynchronous, and Batch Transform inference

Multi-model and multi-container endpoints

Auto scaling and instance/accelerator right-sizing

Deployment strategies — blue/green, canary, shadow tests

CI/CD with SageMaker Pipelines, Model Registry, CodePipeline

Infrastructure as Code with CloudFormation/CDK

Containerization with ECR, ECS, and EKS for ML

Monitoring, Security & Maintenance

2 weeks

SageMaker Model Monitor — data drift and quality drift

CloudWatch metrics, logs, and alarms for endpoints

Automated retraining triggers and pipelines

IAM roles, least privilege, and SageMaker Role Manager

Encryption (KMS), VPC isolation, and network controls

Cost monitoring and optimization for ML workloads

📚 Recommended Study Resources

AWS Skill Builder

Official MLA-C01 exam prep plan with SageMaker labs and the standard exam question set

Stephane Maarek & Frank Kane

Comprehensive MLA-C01 video course covering data engineering through MLOps

AWS Documentation

Amazon SageMaker Developer Guide — Pipelines, Model Registry, and Model Monitor

Tutorials Dojo

MLA-C01 practice exams with detailed explanations for every SageMaker feature

Ready to Test Your Knowledge?

Take our MLA-C01 practice exam covering data prep, model development, deployment, and MLOps on AWS

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🤖 AWS Machine Learning Engineer – Associate