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The new AWS Certified Machine Learning Engineer – Associate MLA-C02 exam reflects the expanding responsibilities of machine learning engineers across traditional machine learning and modern generative AI environments. Candidates preparing for the updated certification can use the latest AWS Certified Machine Learning Engineer – Associate MLA-C02 Practice Tests from Passcert, which cover the key core concepts of the revised exam blueprint and help you prepare effectively for data preparation, ML and foundation model development, Amazon Bedrock, RAG, AI workflow deployment, monitoring, security, and other important MLA-C02 topics.

The MLA-C02 exam validates your ability to build, operationalize, deploy, and maintain machine learning and AI solutions on AWS. Compared with the previous MLA-C01 exam, the updated version significantly expands coverage of generative AI technologies, including foundation models, large language models, retrieval-augmented generation, and agentic AI.

MLA-C02 Replaces the MLA-C01 Exam

AWS is transitioning the AWS Certified Machine Learning Engineer – Associate certification from MLA-C01 to MLA-C02.

Registration for the updated MLA-C02 beta opened September 1, 2026, with beta exam delivery beginning September 29, 2026.

Candidates preparing for the existing version should pay particular attention to the transition schedule:

The MLA-C02 beta is currently available in English only.

Candidates who prefer to complete MLA-C01 can still take the English exam through September 28, 2026. MLA-C01 in Japanese, Korean, and Simplified Chinese will remain available until MLA-C02 reaches general availability.

Importantly, candidates who earn the AWS Certified Machine Learning Engineer – Associate certification by passing MLA-C01 will keep their certification for its normal validity period. They do not need to retake MLA-C02 simply because AWS has introduced a new exam version.

What Is New in MLA-C02 AWS Certified Machine Learning Engineer - Associate?

MLA-C02 retains the same four-domain structure as MLA-C01, but AWS has updated the tasks and skills measured to reflect current machine learning engineering practices.

One of the biggest changes is the much stronger integration of generative AI into the certification.

Important new and expanded areas include:

• Amazon Bedrock and generative AI implementation
• Retrieval-Augmented Generation (RAG) architectures
• Foundation models and LLMs, including selection, customization, and operationalization
• Agentic AI, including agents and multistep AI workflows
• Embeddings and vector databases
• Responsible AI practices
• GenAI monitoring, security, and cost optimization
• MLOps practices extended to foundation models and AI applications

Traditional machine learning remains an essential part of the certification, but MLA-C02 now expects candidates to understand how traditional ML and generative AI technologies work together in production AWS environments.

AWS MLA-C02 Exam Overview

The standard MLA-C02 exam is expected to replace the beta after AWS completes the beta testing process and announces general availability.

Who Should Take the MLA-C02 Exam?

MLA-C02 is intended primarily for professionals responsible for implementing and operating machine learning and AI workloads on AWS.

AWS recommends approximately one year of experience in machine learning engineering or a related field, together with hands-on experience using AWS services.

The updated certification is particularly relevant to Machine Learning Engineers, MLOps Engineers, LLMOps Engineers, Data Engineers, Software Developers, Data Scientists, and other technical professionals working with production AI and ML environments.

Candidates should ideally have experience with both traditional machine learning and generative AI services, especially Amazon SageMaker AI and Amazon Bedrock.

AWS MLA-C02 Exam Domains

The MLA-C02 exam contains four domains. Although the overall structure remains similar to MLA-C01, the content has been updated to incorporate AI, foundation models, RAG, and modern ML engineering practices.

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

This domain focuses on preparing reliable data for both traditional machine learning and generative AI solutions. Candidates should understand data ingestion, transformation, feature engineering, data quality, storage selection, embeddings, vector databases, and preparing documents or datasets for RAG and foundation model workloads.

Domain 2: ML Model and Foundation Model Development – 24%

This section covers selecting, training, customizing, and evaluating both traditional ML models and foundation models. Candidates should understand model selection, hyperparameter tuning, prompt-related approaches, fine-tuning, RAG architectures, model evaluation, and how to choose solutions based on accuracy, latency, performance, and cost requirements.

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

Domain 3 measures your ability to move ML and AI solutions into production. Topics include model deployment, compute selection, CI/CD pipelines, infrastructure automation, Amazon Bedrock Knowledge Bases, foundation model deployment, RAG workflows, and deploying and orchestrating AI agents.

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

The final domain focuses on keeping production ML and AI environments reliable, secure, and cost-efficient. Candidates should understand model and infrastructure monitoring, AI agent observability, troubleshooting, security controls, IAM, encryption, responsible AI safeguards, Amazon Bedrock Guardrails, and cost optimization for ML and GenAI workloads.

How to Prepare for the AWS MLA-C02 Exam

A strong MLA-C02 preparation strategy should combine traditional ML engineering fundamentals with the new GenAI-focused objectives.

Start by reviewing the four official domains and identifying any weak areas. Build a solid foundation in data preparation, SageMaker AI, model development, deployment, MLOps, monitoring, and AWS security before moving deeper into Amazon Bedrock, RAG, embeddings, foundation models, agents, and responsible AI.

Hands-on practice is especially useful because many MLA-C02 topics involve choosing between several technically possible AWS architectures.

Scenario-based practice can then help reinforce those concepts. The latest Passcert MLA-C02 Practice Tests cover the core knowledge areas in the updated blueprint and can be used alongside the official AWS exam guide to evaluate your readiness, identify weaker topics, and focus your remaining preparation time more efficiently.

MLA-C01 or MLA-C02: Which Exam Should You Prepare For?

Candidates who are already close to completing their MLA-C01 preparation still have the option to take MLA-C01 in English through September 28, 2026.

However, candidates who are only beginning their preparation should consider focusing directly on MLA-C02, especially if they want to develop skills in Amazon Bedrock, generative AI, foundation models, RAG, and agentic AI.

Both versions lead to the same AWS Certified Machine Learning Engineer – Associate certification. The key difference is the knowledge measured by the exam version.

Your Next Steps: Ready for the MLA-C02 Exam?

The AWS Certified Machine Learning Engineer – Associate MLA-C02 exam represents an important evolution of AWS machine learning certification.

It preserves the core skills that ML engineers need to prepare data, develop models, deploy workloads, automate pipelines, monitor systems, and protect AWS environments. At the same time, it extends those responsibilities into Amazon Bedrock, generative AI, RAG, foundation models, LLMs, vector search, agentic AI, and responsible AI.

For candidates starting their preparation now, understanding the relationship between traditional machine learning and these newer AI technologies will be more valuable than studying either area in isolation.

With MLA-C01 in English available only through September 28, 2026, MLA-C02 is becoming the new path for professionals seeking the AWS Certified Machine Learning Engineer – Associate certification.