MLA-C01 (English) is no longer available — it retired on September 28, 2026. MLA-C02 has replaced it, and this material has not yet been updated for it.
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Every in-scope SageMaker feature, MLOps workflow, monitoring and security decision the MLA-C01 exam actually tests — organised by the four exam domains with runbooks and decision tables
The AWS Certified Machine Learning Engineer Associate (MLA-C01) is AWS's certification for practitioners who build, deploy, and maintain ML solutions. Launched in 2024, it focuses on the practical engineering side of ML — data pipelines, model training, deployment, monitoring, and MLOps on AWS.
This exam targets ML engineers, data engineers, and software engineers who build and deploy ML models on AWS. Unlike the AI Practitioner (which is conceptual), this certification requires hands-on experience with SageMaker, data pipelines, and model deployment. It's a solid credential for anyone transitioning into ML engineering from a software or data background.
Read the full AWS Machine Learning Engineer Associate certification guide
The MLA-C01 exam has 65 questions (50 scored, 15 unscored) in multiple-choice and multiple-response format. You get 130 minutes, and the passing score is 720/1000 (approximately 72%). The exam costs $150 USD. MLA-C01 retired in English on September 28, 2026; its replacement, MLA-C02, is in beta with 85 questions in 170 minutes at $75 USD.
Recommended experience: One to two years of experience building, deploying, and maintaining ML models, with hands-on experience using Amazon SageMaker and related AWS data services.
The AI Practitioner (AIF-C01) is a foundational, conceptual exam — no coding required. The ML Engineer Associate (MLA-C01) requires hands-on engineering skills: building data pipelines, training models with SageMaker, deploying endpoints, and implementing MLOps practices. It's a significant step up in difficulty and depth.
Master Amazon SageMaker (training, endpoints, pipelines, feature store), AWS Glue (ETL), Step Functions (orchestration), S3 (data storage), CloudWatch (monitoring), and IAM (security). Also understand SageMaker built-in algorithms, hyperparameter tuning, and model monitoring for drift detection.
AWS retired the Machine Learning Specialty (MLS-C01) and replaced it with two exams: the AI Practitioner (foundational) and the ML Engineer Associate (hands-on). If you had the ML Specialty, the MLA-C01 is the closest replacement for proving your practical ML skills.
Largely, yes. These questions target MLA-C01, and most of that content carries over to MLA-C02: data preparation, SageMaker algorithms, deployment options, MLOps, and monitoring. MLA-C02 adds Amazon Bedrock, RAG, agents, and generative AI evaluation, which you will need to study separately.
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