MLA-C02

MLA-C02: AWS ML Engineer Associate

Everything you need for AWS Certified Machine Learning Engineer Associate MLA-C02: format, passing score, the four domains and free sample questions.

Passing score
720 / 1000
Level
Associate
Validity
3 years
Status
Beta pricing

MLA-C02 is the AWS Certified Machine Learning Engineer – Associate exam. AWS describes it as validating the ability to build, operationalise, deploy and maintain AI and ML solutions and pipelines on AWS — working with both traditional ML models and foundation models.

This is a builder’s exam. AWS expects at least one year of experience with Amazon SageMaker AI, Amazon Bedrock and related services, plus at least a year in a role such as backend developer, DevOps engineer, data engineer or data scientist. If you use AI on AWS but do not build it, AIF-C01 is the exam that matches your work.

What the exam covers

DomainWeight
Data Preparation for ML and AI28%
ML Model and Foundation Model (FM) Development24%
Deployment and Orchestration of ML and AI Workflows24%
Operating, Monitoring, and Securing ML and AI Solutions24%

Almost flat, with data preparation slightly ahead. That shape is itself the message: this is an engineering certification, not a modelling one. Three quarters of it is about getting data ready, shipping models and running them in production — only a quarter is about developing the models themselves.

Note also what AWS added in C02 relative to its predecessor: foundation models sit alongside traditional ML throughout, and agentic workflows and observability appear explicitly in the task list.

Where to start

Read the prerequisites honestly — AWS’s one-year expectation is real for this exam. Then the exam format, which has a beta wrinkle worth knowing before you book.

MLA-C02 guides

  1. MLA-C02 exam format: questions, score and costThe MLA-C02 format: beta pricing at $75, 170 minutes and 85 questions, passing score 720, compensatory scoring and what the exam guide says.
  2. MLA-C02 vs AIF-C01: which AWS AI exam?MLA-C02 is associate level for engineers who build ML on AWS. AIF-C01 is foundational for people who use it. How to choose between them.
  3. MLA-C02 study plan: pass in 6 weeksA six-week MLA-C02 study plan across the four AWS domains, with hands-on SageMaker AI and Bedrock work rather than reading.
  4. Free MLA-C02 sample questions with answersFive free AWS Certified Machine Learning Engineer Associate MLA-C02 practice questions across the four domains, each with an explained answer.
  5. MLA-C02 data preparation for ML and AIThe largest MLA-C02 domain explained: ingestion, transformation, feature engineering, validation, splitting and preparing data for foundation models.
  6. MLA-C02 model and FM developmentThe MLA-C02 development domain explained: choosing an approach, training, hyperparameter tuning, evaluation metrics, versioning and foundation model work.
  7. MLA-C02 deployment and orchestrationThe MLA-C02 deployment domain explained: endpoint types, auto scaling, real-time versus batch inference, CI/CD for ML and agentic workflows.
  8. MLA-C02 operating, monitoring and securingThe MLA-C02 operations domain explained: drift detection, model and infrastructure monitoring, agent observability, cost optimisation and ML security.
  9. MLA-C02 prerequisites: what you need firstMLA-C02 has no formal prerequisites, but AWS expects a year with SageMaker AI and Bedrock plus a year in an engineering role. What that means in practice.
  10. Is MLA-C02 worth it? Who should take itIs AWS Certified Machine Learning Engineer Associate worth taking? Who benefits, what beta pricing means, and when AIF-C01 fits better.