Exam Details
AWS · MLA-C02
Prepare for MLA-C02: Build, train, deploy, and operationalize ML and generative AI solutions on AWS using SageMaker AI and Amazon Bedrock.
Overview
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FAQ
The MLA-C02 certification validates your ability to build, train, deploy, and maintain machine learning and generative AI solutions on AWS. This associate-level certification targets ML engineers and MLOps engineers who perform exploratory data analysis, feature engineering, model training, foundation model fine-tuning, RAG implementation, and MLOps/LLMOps using Amazon SageMaker AI and Amazon Bedrock.
You'll demonstrate skills in data preparation for ML and AI, model and foundation model development, deployment and orchestration of ML and AI workflows, and operating, monitoring, and securing ML and AI solutions. As organizations operationalize AI at scale, engineers who bridge data science, generative AI, and production deployment are essential to every ML team.
You're a fit for MLA-C02 if you:
Not sure if you're ready? Try a free sample quiz: 10 questions, instant results, identify weak areas.
| Domain | Weight | What This Means |
|---|---|---|
| Data Preparation for ML and AI | 28% | Build data ingestion pipelines, perform feature engineering, manage datasets, configure vector databases, prepare documents for RAG, and handle multimodal data |
| ML Model and Foundation Model (FM) Development | 24% | Select algorithms and foundation models, train and fine-tune models, evaluate performance, apply SageMaker built-in tools, and choose RAG architectures |
| Deployment and Orchestration of ML and AI Workflows | 24% | Deploy models and agents to endpoints, implement CI/CD for MLOps and LLMOps, orchestrate agentic workflows, and configure auto scaling |
| Operating, Monitoring, and Securing ML and AI Solutions | 24% | Monitor model and agent performance, detect drift, manage costs, secure AI systems, and implement guardrails |
You're probably ready if you can:
You might need more prep if:
If this feels too advanced:
If you know the basics but want to build confidence:
The standard MLA-C02 exam consists of 65 questions to be completed within 130 minutes. Fifteen questions are unscored and used for future exam development, leaving 50 scored questions. Question types are multiple choice and multiple response only. Ordering and matching items, which appeared on MLA-C01, were removed from MLA-C02.
The beta version of MLA-C02, delivered from September 29, 2026, has a different format: 85 questions in 170 minutes. The beta is English-only and costs $75 USD. The standard version launches January 14, 2027 in all supported languages.
The exam uses a scaled scoring system from 100 to 1,000, with a minimum passing score of 720. AWS uses compensatory scoring with no per-domain minimum. You need to pass only the overall exam. The beta exam does not report a pass or fail result. AWS collects question quality data before finalizing scores. ExamOS Mock Exams and Rookie, Challenger, and Legend practice modes help you prepare for the test environment.
The exam content is divided into four domains reflecting the lifecycle of an AI and ML project:
| Domain | Weight |
|---|---|
| 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% |
Two points moved from Domain 2 to Domain 3 compared to MLA-C01, reflecting the growing weight of deployment and orchestration skills.
The domain structure is preserved, but the scope now includes generative AI, foundation models, LLMs, agentic AI, and Amazon Bedrock. New skills include configuring vector databases, preparing documents for RAG applications, fine-tuning foundation models, NLP evaluation metrics (BLEU, ROUGE, BERTScore), and agent deployment with Bedrock AgentCore. Removed skills include SageMaker Neo edge optimization, bring-your-own-container, and traditional model compression.
The MLA-C01 exam retired in English on September 28, 2026. It remains available in Japanese, Korean, and Simplified Chinese during the beta period. Once MLA-C02 reaches general availability on January 14, 2027, MLA-C01 retires in all languages. Certifications earned under MLA-C01 remain valid for three years.
Successful candidates use a combination of official and community resources:
The standard MLA-C02 exam costs $150 USD. The beta exam costs $75 USD. Additional taxes may apply based on your local jurisdiction. If you have previously passed any AWS certification, check your AWS Certification Account for a 50% discount voucher for your next exam.
If you are unsuccessful on your first attempt, you must wait 14 calendar days before you are eligible to retake the exam. There is no limit on the number of attempts, but you must pay the full registration fee for each subsequent attempt. Once you pass, you cannot retake the same exam for two years.
The certification is valid for three years from the date you pass the exam. To maintain your certified status, you must recertify before your expiration date. You can recertify by:
AWS does not accept continuing education credits as an alternative.
This certification is intended for professionals who build and operationalize ML and generative AI solutions in production environments. While there are no formal prerequisites, the target candidate has:
Demand for professionals who bridge data science and production engineering continues to grow. The certification opens several high-value roles:
Once you have mastered the Associate level, you can further specialize: