Directory / Amazon Web Services

AWS Certified Machine Learning Engineer – Associate

The original version of the ML Engineer Associate exam: building, deploying and operating classical ML workloads on SageMaker, with very little generative AI.

What it signals

Practical assessment: Scenario-based multiple choice. Proctored scenario-based selected-response exam with ordering/matching items but no lab or code.
Identity assurance: Proctored with identity verification. Pearson VUE testing centre or online proctored
Depth: Associate. Associate level.
Experience expected: About 1 year. Exam guide recommends at least 1 year of experience.
Portability: Mixed. ML lifecycle concepts transfer but questions are framed around SageMaker and AWS services.
Verifiability: Public badge or registry. Credly digital badge with verification.

For hiring managers

Almost every holder on the market as of late 2026 passed MLA-C01, which is a classical ML / SageMaker MLOps exam with very little generative AI. The badge name does not change with the version - ask the earn date.
De facto successor to the retired ML Specialty, but at a lower (Associate) level and more operations-focused than modelling-focused.
Designing end-to-end ML architectures and deep work in NLP/computer vision are officially out of scope.

Verifying it

Credly digital badge link shared by the candidate. No public registry.

What it covers

Published exam outline

Data Preparation for Machine Learning (ML) 28%
ML Model Development 26%
Deployment and Orchestration of ML Workflows 22%
ML Solution Monitoring, Maintenance, and Security 24%

By topic

GenAI apps: Touched on (under 10%). MLA-C01 only touches foundation models (e.g. fine-tuning pre-trained models via Bedrock/JumpStart) within Domain 2.
Agents: Not covered. No agent content in the MLA-C01 blueprint.
Classical ML: Substantial (20 to 35%). Domain 2 ML Model Development is 26%.
Operations: Core focus (over 35%). Domain 3 (22%) plus the monitoring and maintenance portion of Domain 4 (24%) put deployment and operations above 35%.
Data: Substantial (20 to 35%). Domain 1 Data Preparation is 28%.
Responsible AI: Some (10 to 20%). Security is one of three tasks in Domain 4 and bias handling sits in Domain 1; estimated 10-15%, no official sub-weight.

Exam details

Who the provider says it is for. Backend software developers, DevOps engineers, data engineers, MLOps engineers and data scientists who build, operationalise, deploy and maintain ML solutions on AWS.

Format: multiple choice; multiple response; ordering; matching
Delivery: Pearson VUE testing centre or online proctored
Code or hands-on work: No labs and no code writing. All items are selected-response (choice, ordering, matching); scenario questions may reference configurations. Whether code snippets appear in questions is not stated in the exam guide.
Prerequisites: None
Recommended experience: At least 1 year using Amazon SageMaker and other AWS services for ML engineering, plus at least 1 year in a related role (backend software developer, DevOps developer, data engineer, data scientist).
Renewal: Pass the latest version of the exam, or pass AWS Certified Generative AI Developer - Professional; Skill Builder maintenance option also described on the recertification page.

Preparing

Not confirmed

These details were not available from an official source at the time of research.

End date for non-English MLA-C01 delivery
Whether code snippets appear in exam items
Share of Domain 4 devoted to security (no official sub-domain weights)

Sources (4)