AWS Certified Machine Learning Engineer – Associate
Shows you can build, deploy and run machine learning solutions on AWS. The updated version adds generative AI and agent topics.
Good to know: AWS lists 28 September 2026 as the last day for MLA-C01 in English. The updated MLA-C02 was listed as beta, with delivery beginning 29 September 2026. Check AWS for its current status and exam code before you book.
- Issuer
- Amazon Web Services
- Level
- Associate
- Exam code
- MLA-C01 / MLA-C02
- Exam format
- Updated exam (MLA-C02): 85 questions · 170 minutes. The earlier MLA-C01 had 65 questions · 130 minutes.
- Valid for
- 3 years
- Focus
- Machine learning engineering
- Who it is for
- ML engineers, MLOps and LLMOps engineers, data engineers, backend developers and data scientists.
- Before you start
- AWS suggests at least 1 year in machine learning engineering or a related field and 1 year of hands-on AWS experience.
What the exam covers
Listed as the issuer lists them, with weights where the issuer publishes them.
- Generative AI implementation (Amazon Bedrock, RAG designs)
- Agentic AI: orchestrating agents and complex workflows
- Foundation models and LLMs: choosing, fine-tuning and operating them
- Responsible AI practices
Key terms to know
Words from this certification's topic areas, in plain English. Know these and the study material will be much easier to follow.
- Machine learning
- Software that learns patterns from examples instead of following hand-written rules.
- Feature engineering
- Turning raw data into inputs that help a model learn.
- Hyperparameter
- A setting you choose before training starts, such as how fast the model learns.
- MLOps
- The practices for deploying, monitoring and updating machine learning models in production.
- Model drift
- When a model gets worse over time because real-world data changes.
- Inference
- Running a trained model to get an answer.
- Foundation model
- A large model trained on broad data that can be adapted to many tasks.
- Fine-tuning
- Training an existing model further on your own examples.
- Retrieval-augmented generation (RAG)
- Looking up relevant documents first, then giving them to the model to answer from.
- AI agent
- Software that uses a model to plan steps and use tools to reach a goal.
- Orchestration
- Coordinating the steps and agents in a workflow.
- Responsible AI
- Building and using AI fairly, safely, transparently and with accountability.
- Amazon SageMaker AI
- An AWS service for building, training and deploying machine learning models.
- Amazon Bedrock
- An AWS service for using foundation models through an API.
Build the knowledge here
Our lessons are not exam prep for this certification, but they cover the same ground.
Details last checked Oct 7, 2026 against the issuer's own pages. Exams, versions and rules change. Confirm on the official page before you book. We are not affiliated with Amazon Web Services.