AWS Certified Machine Learning Engineer – Associate (MLA-C02) Exam Guide 2026

Important Update (2026): MLA-C01 is being replaced by MLA-C02. AWS is updating the AWS Certified Machine Learning Engineer – Associate certification to reflect the evolving role of modern ML engineers.

The updated MLA-C02 exam expands beyond traditional machine learning engineering to include Generative AI, Amazon Bedrock, Retrieval-Augmented Generation (RAG), Agentic AI, Foundation Models, Large Language Models (LLMs), and Responsible AI.

Registration for the updated exam MLA-C02 beta opened on September 1, 2026. During the beta phase, the exam is registered under the temporary beta code ME1-C02.

The last day to take the current MLA-C01 exam in English is September 28, 2026, while beta delivery of MLA-C02 begins on September 29, 2026.

If you already hold or pass the MLA-C01 certification before its retirement, your certification remains valid for its full 3-year validity period.

The MLA-C02 beta is currently available in English only. Additional languages, including Japanese, Korean, and Simplified Chinese, are planned for the general availability release.

If you are starting your preparation now, MLA-C02 is an excellent choice for validating both traditional ML engineering and modern GenAI/LLM skills on AWS.

By the end of this guide, you’ll have a clear understanding of what to expect from the AWS Certified Machine Learning Engineer – Associate MLA-C02 exam and how this certification can strengthen your career in machine learning, MLOps, LLMOps, and Generative AI.

What’s New in MLA-C02?

The updated MLA-C02 exam reflects the broadened scope of the modern ML engineer role.

The four-domain structure remains the same, but the existing task statements and skills have been updated to align with current industry practices.

Key additions include:

• Generative AI implementation – Building and deploying generative AI solutions using AWS services.
• Amazon Bedrock – Using Amazon Bedrock for foundation model access, customization, and GenAI application development.
• Retrieval-Augmented Generation (RAG) – Implementing architectures that combine foundation models with enterprise or external knowledge sources.
• Agentic AI – Orchestrating AI agents and building complex, multi-step AI workflows.
• Foundation Models and LLMs – Selecting, customizing, fine-tuning, and operationalizing foundation models and large language models.
• Responsible AI – Applying responsible AI practices across traditional ML and generative AI workloads.
• LLMOps – Applying operational practices to modern LLM and GenAI workloads.
• Modern ML engineering – Expanding traditional ML engineering practices to support increasingly complex AI workloads.

MLA-C01 vs MLA-C02: A Quick Comparison

Key Dates at a Glance

Important: During the Beta phase, the updated MLA-C02 exam is registered under the temporary exam code ME1-C02. Once the exam reaches General Availability, it will officially be available as MLA-C02.

What is the AWS Certified Machine Learning Engineer – Associate MLA-C02 Exam?

The AWS Certified Machine Learning Engineer – Associate (MLA-C02) is an associate-level certification offered by Amazon Web Services (AWS).

The MLA-C02 exam validates a candidate’s ability to build, operationalize, deploy, and maintain AI and ML solutions and pipelines using the AWS Cloud. It covers ML engineering skills across traditional machine learning models and foundation models.

The updated MLA-C02 exam expands the scope of the certification to include modern AI workloads such as:

• Generative AI
• Foundation Models
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Agentic AI
• Amazon Bedrock
• Responsible AI

The certification is designed for professionals who build and operationalize ML and generative AI solutions in production environments. AWS recommends at least one year of experience using Amazon SageMaker AI, Amazon Bedrock, and other AWS services for ML engineering, along with experience in a related role and both traditional ML and generative AI.

Topics Included in MLA-C02 Exam

The AWS Certified Machine Learning Engineer – Associate MLA-C02 exam retains the four-domain structure of the current MLA-C01 certification.

However, the skills and task statements within those domains have been updated to reflect modern machine learning and AI engineering practices.

Domain 1: Data Preparation for ML and AI – 28%

This domain focuses on preparing and managing data for machine learning and AI workloads.

Key topics include:

• Collecting, storing, and organizing data for ML and AI solutions
• Data ingestion and transformation
• Feature engineering and preprocessing
• Data validation and quality checks
• Identifying and mitigating data bias
• Preparing data for traditional ML, generative AI, and foundation model use cases
• Preparing data for applications such as Retrieval-Augmented Generation (RAG)

Domain 2: ML Model and Foundation Model (FM) Development – 24%

This domain covers the development, evaluation, and optimization of traditional ML models as well as foundation models.

Key topics include:

• Selecting appropriate ML algorithms and techniques
• Training, tuning, and evaluating ML models
• Model performance optimization
• Working with foundation models (FMs) and large language models (LLMs)
• Selecting and customizing foundation models for specific use cases
• Generative AI and Amazon Bedrock capabilities
• Foundation model customization and fine-tuning
• Retrieval-Augmented Generation (RAG) solutions
• Responsible AI considerations for ML and generative AI solutions

Domain 3: Deployment and Orchestration of ML and AI Workflows – 24%

This domain focuses on deploying and orchestrating ML and AI solutions in production environments.

Key topics include:

• Selecting appropriate infrastructure, compute, and deployment strategies
• Deploying ML models and foundation models
• Real-time and batch inference
• Scaling and optimizing model deployments
• Automating ML workflows and CI/CD processes
• Orchestrating training, inference, and retraining workflows
• Deploying and managing agentic AI workflows
• Deploying and managing RAG-based solutions

Domain 4: Operating, Monitoring, and Securing ML and AI Solutions – 24%

This domain focuses on operating ML and AI solutions efficiently while maintaining security, reliability, and performance.

Key topics include:

• Monitoring ML models and AI solutions
• Monitoring agentic AI workflows, data, and infrastructure
• Troubleshooting and optimizing ML and AI workloads
• Monitoring model performance and detecting issues
• Implementing security and access controls
• Protecting data and ML/AI resources
• Applying responsible AI and security best practices

Amazon Bedrock in MLA-C02

One of the major additions to MLA-C02 is the expanded coverage of Amazon Bedrock.

Candidates should become familiar with how Amazon Bedrock can be used to build and operationalize Generative AI applications.

Important areas include:

• Foundation models
• Model selection
• Generative AI application development
• Model customization
• Fine-tuning concepts
• RAG architectures
• AI agents
• Agentic workflows
• Security
• Monitoring
• Responsible AI
• Production deployment considerations

Foundation Models and LLMs

MLA-C02 expands the certification’s scope into foundation models and large language models.

Candidates should understand how to:

• Select an appropriate foundation model
• Identify model capabilities and limitations
• Customize foundation models
• Understand fine-tuning
• Operationalize LLM workloads
• Evaluate model performance
• Integrate LLMs into applications
• Select appropriate approaches for GenAI use cases
• Consider security, cost, latency, and performance requirements

Agentic AI in MLA-C02

Another major addition is Agentic AI.

Modern AI applications can use agents to perform multiple steps, interact with tools, retrieve information, and execute complex workflows.

Candidates should understand:

• What AI agents are
• Agent orchestration
• Multi-step workflows
• Tool integration
• AI agent decision-making
• Workflow automation
• Agent-based application architectures
• Monitoring and operational considerations
• Security and responsible AI considerations for agents

AWS MLA-C02 Hands-on Preparation

Hands-on experience is an important part of preparing for the AWS Certified Machine Learning Engineer – Associate certification.

Candidates should practice working with AWS services used across the ML and GenAI lifecycle.

Recommended areas include:

• Amazon SageMaker AI
• Amazon Bedrock
• Amazon S3
• AWS Glue
• SageMaker Data Wrangler
• SageMaker Ground Truth
• SageMaker Pipelines
• SageMaker Model Monitor
• SageMaker Clarify
• Amazon CloudWatch
• AWS IAM
• AWS Lambda
• Amazon ECS
• Amazon EKS
• AWS CloudFormation
• AWS CDK
• Amazon EventBridge
• AWS CI/CD services

For MLA-C02 specifically, candidates should add practical exposure to:

• Generative AI applications
• Foundation models
• LLM workflows
• RAG architectures
• Amazon Bedrock
• AI agents
• Agentic workflows
• Responsible AI

Why Pursue AWS Certified Machine Learning Engineer – Associate MLA-C02?

The AWS Certified Machine Learning Engineer – Associate certification validates your ability to implement and operationalize machine learning workloads on AWS.

With MLA-C02, the certification also demonstrates exposure to the technologies shaping the next generation of AI engineering, including:

• Generative AI
• Foundation Models
• LLMs
• RAG
• Agentic AI
• Amazon Bedrock
• Responsible AI

This makes MLA-C02 particularly relevant for professionals looking to build careers across ML engineering, MLOps, LLMOps, data engineering, and AI application development.

The certification can help demonstrate practical knowledge of building, deploying, monitoring, and maintaining modern AI workloads on AWS.

Career Paths and Opportunities

MLA-C02 Beta Exam Information

The MLA-C02 Beta exam allows candidates to take the updated certification before its General Availability (GA) release.

During the Beta:

• Available in English only
• Beta exam code: ME1-C02
• 85 questions
• 170 minutes
• Beta price: $75 USD
• May include additional statistical evaluation questions that do not affect the score
• Beta results are typically available within 5 business days

The MLA-C02 General Availability (GA) exam is scheduled to begin on January 14, 2027, under the official MLA-C02 exam code.

Exam Results for AWS Certified Machine Learning Engineer – Associate MLA-C02

AWS certification exams use a scaled scoring system.

Candidates should refer to the official MLA-C02 exam guide for the current scoring and exam-result details applicable to the updated examination.

The important point is that certification decisions are based on the overall exam result rather than requiring candidates to pass every individual domain separately.

AWS may also provide domain-level performance information to help candidates understand areas of relative strength and weakness.

MLA-C01 or MLA-C02: Which Exam Should You Take?

If you are already well prepared for MLA-C01, you can still take the current English exam before September 28, 2026.

Passing MLA-C01 before retirement does not shorten the certification’s validity. Your certification remains active for its full 3-year validity period.

However, candidates starting preparation now may prefer MLA-C02 because it reflects the broader modern ML engineering landscape.

Choose MLA-C01 if:

• You are already deeply prepared for MLA-C01.
• You want to take the current exam before it retires.
• You are comfortable with traditional ML engineering topics.
• You want to complete the certification before September 28, 2026.

Choose MLA-C02 if:

• You are starting your preparation now.
• You want exposure to Generative AI.
• You want to learn Amazon Bedrock.
• You want to understand RAG architectures.
• You want foundation model and LLM knowledge.
• You are interested in Agentic AI.
• You are targeting AI Engineer, MLOps, or LLMOps roles.
• You want a certification aligned with modern AI engineering practices.

Conclusion

The AWS Certified Machine Learning Engineer – Associate (MLA-C02) is the updated version of AWS’s associate-level ML engineering certification.

The exam retains its four-domain structure while expanding its focus to Generative AI, foundation models, LLMs, Amazon Bedrock, RAG, agentic AI, deployment, monitoring, security, and responsible AI.

Candidates can take MLA-C01 English until September 28, 2026, while MLA-C02 GA delivery begins January 14, 2027.

Overall, MLA-C02 validates practical skills for building, deploying, monitoring, and securing modern ML and AI solutions on AWS.