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AWS · AI & Machine Learning · Associate

AWS Certified Machine Learning Engineer - Associate exam guide

Exam MLA-C02 (beta from Sep 29, 2026; general availability Jan 14, 2027 when MLA-C01 retires)

AWS Certified Machine Learning Engineer - Associate proves you can prepare data, build, deploy, operate and secure ML and AI workloads on AWS, primarily with SageMaker and Amazon Bedrock. The MLA-C02 version adds generative AI, RAG, fine-tuning and agentic AI orchestration, making it AWS's core hands-on credential for ML and AI engineers.

Questions85 (65 scored, 20 unscored) on the MLA-C02 beta
Time limit170 minutes
Passing score720 (100-1000 scale)
Exam fee$150 USD
Valid for3 years
DeliveryPearson VUE test center or online proctored

Format: Multiple choice, multiple response, ordering, matching and case study. Fee note: Beta exam is $75 USD; standard associate price is $150. AWS Certified holders get 50% off their next exam..

Who it's for

ML engineers, data scientists and MLOps engineers with about a year of experience building and operating ML solutions on AWS.

What the exam covers

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%

Eligibility & prerequisites

Required: None

Recommended: At least 1 year of experience using Amazon SageMaker and other AWS services for ML engineering, plus a background in a related role such as backend developer, data engineer or data scientist.

Keeping it active

Valid for 3 years. Recertify by passing the latest version of the exam or a higher-level AWS certification that recertifies it.

Skills & careers

Skills it validates

  • Ingesting, transforming and feature-engineering data for ML
  • Training, tuning and evaluating models in SageMaker
  • Customizing foundation models with fine-tuning and RAG in Amazon Bedrock
  • Orchestrating agentic AI workflows
  • Deploying endpoints and building CI/CD ML pipelines
  • Monitoring drift, cost and security of ML and AI workloads

Job roles

Machine Learning EngineerMLOps EngineerData ScientistAI EngineerData EngineerCloud AI Developer

Where it fits in your path

Planning a degree too? See how this cert earns college credit in the College Credit Navigator.

Build my study plan

  1. Week 1-2: Data preparation: S3, Glue, Data Wrangler, Feature Store; build a feature pipeline for a sample dataset.
  2. Week 3-4: Model development: SageMaker built-in algorithms, training jobs, hyperparameter tuning; then Bedrock fine-tuning and knowledge bases for RAG.
  3. Week 5: Deployment and orchestration: real-time, serverless, async and batch inference; SageMaker Pipelines and agent orchestration.
  4. Week 6: Operations and security: Model Monitor, Clarify, CloudWatch, IAM, VPC and encryption for ML workloads.
  5. Week 7-8: Use the MLA-C02 exam guide on AWS Skill Builder, take practice exams, and fill gaps in the new generative and agentic AI tasks.

Make it yours: pick your exam date and weekly hours. We'll split your time by domain weight and give you a printable plan.

Exam-day tips

  • Confirm which version you are booking: MLA-C01 retired in English after Sep 28, 2026; MLA-C02 is the current version going forward.
  • MLA-C02 keeps the four-domain structure but adds RAG, fine-tuning and agentic AI; older C01-only prep material will leave gaps.
  • Know SageMaker inference options (real-time, serverless, asynchronous, batch transform) and when each is cheapest.
  • Expect cost-optimization questions: spot training, multi-model endpoints, right-sizing instances.
  • With 170 minutes for 85 questions you have time; flag case-study items and come back rather than rushing.

Key terms for this exam

All 115 terms Study with thinQcards™

Commonly confused on this exam

Frequently asked questions

What changed in MLA-C02?

AWS added generative AI implementation (fine-tuning, RAG), agentic AI orchestration, foundation model selection and expanded Bedrock coverage. The four-domain structure is unchanged.

How much does the ML Engineer Associate exam cost?

The standard associate price is $150 USD; the MLA-C02 beta was offered at $75.

How hard is the AWS ML Engineer Associate?

It is a hands-on associate exam. Candidates without real SageMaker experience usually need 6-10 weeks of lab-heavy study.

Does it renew my AI Practitioner?

Yes. Earning the ML Engineer - Associate also recertifies the AWS Certified AI Practitioner.

ML Engineer Associate vs Google Professional ML Engineer?

Both validate end-to-end ML engineering. The AWS exam centers on SageMaker and Bedrock; Google's centers on its own ML platform. Choose by the cloud you work in.

Official page: aws.amazon.com. Reviewed November 2026. Vendors change fees and objectives, so confirm with the vendor before you book. Spot something out of date? Tell us.