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AWS Certified Machine Learning Engineer – Associate

The rewritten ML Engineer Associate exam. It keeps the MLOps core and adds foundation models, RAG, vector databases, LLM evaluation and agent operations.

What it signals

Practical assessment: Scenario-based multiple choice. Proctored multiple choice/multiple response only.
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.
Portability: Mixed. Concepts transfer but framed around SageMaker AI and Bedrock.
Verifiability: Public badge or registry. Credly digital badge with verification.

For hiring managers

Substantial rewrite: from late 2026 onward the same certification title covers foundation models, RAG and agent operations in addition to classical ML. Earn date tells you which blueprint the holder was tested on.
Beta candidates get one attempt only; beta exams yield a valid certification.
Still operations-oriented (deploying and running models/agents) rather than application design.

Verifying it

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

What it covers

Published exam outline

Data Preparation for ML and AI 28%
ML Model and Foundation Model (FM) Development 24%
Deployment and Orchestration of ML and AI Workflows 24%
Operating, Monitoring, and Securing ML and AI Solutions 24%

By topic

GenAI apps: Substantial (20 to 35%). FM selection, RAG, prompt engineering and LLM evaluation skills were added across all four domains; estimated 20-30% since no sub-domain weights are published.
Agents: Some (10 to 20%). Agent deployment, state management, agent pipelines and agent monitoring appear as several skills in Domains 3 and 4; estimated 10-15%.
Classical ML: Substantial (20 to 35%). Domain 2 (24%) still covers training, tuning and evaluating models, now shared with FM topics.
Operations: Core focus (over 35%). Domain 3 (24%) plus monitoring/cost tasks in Domain 4 (24%) exceed 35%.
Data: Substantial (20 to 35%). Domain 1 Data Preparation for ML and AI is 28%.
Responsible AI: Some (10 to 20%). Security task in Domain 4 plus bias, data masking and Guardrails skills; estimated 10-15%.

Exam details

Who the provider says it is for. ML engineers, MLOps engineers, LLMOps engineers, data engineers, backend software developers, data scientists.

Format: multiple choice; multiple response
Delivery: Pearson VUE testing centre or online proctored
Code or hands-on work: No labs and no code writing; multiple choice and multiple response only.
Prerequisites: None
Recommended experience: At least 1 year using Amazon SageMaker AI, Amazon Bedrock and other AWS services for ML engineering, plus at least 1 year in a related role; experience with both traditional ML and generative AI.
Renewal: Same as MLA-C01: pass latest version or pass Generative AI Developer - Professional.
Price notes: 75 USD is beta pricing. Standard (GA) price not yet published for MLA-C02; MLA-C01 is 150 USD.

Preparing

Not confirmed

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

GA date, GA price and GA duration for MLA-C02
Beta end date
Whether 'ME1-C02' is the formal registration code for the beta
Exact share of blueprint for GenAI and agents

Sources (4)