MLA-C02 Replaces MLA-C01: What’s New in the AWS Certified Machine Learning Engineer – Associate Exam?
-
- September 02nd, 2026
- 64 views
FREE SEO Topical Map Generator: Find Your Next Content Ideas
AWS is updating the AWS Certified Machine Learning Engineer – Associate certification from MLA-C01 to MLA-C02, bringing the exam closer to the way machine learning engineers now work with both traditional ML and generative AI technologies.
Registration for the MLA-C02 beta exam opened on September 1, 2026, and beta testing begins on September 29, 2026. The current MLA-C01 English exam remains available through September 28, 2026, giving candidates a short transition period to decide which version to take.
The biggest change is not the certification title or domain structure. AWS is expanding the skills measured to include Amazon Bedrock, generative AI, retrieval-augmented generation (RAG), foundation models, LLMs, agentic AI, and responsible AI practices.
Candidates beginning preparation for the new version can also review the updated MLA-C02 AWS Certified Machine Learning Engineer – Associate practice tests and preparation materials from Passcert to become familiar with the revised objectives and new AI-focused topics.
MLA-C02 Transition Dates Candidates Need to Know
| Date | What Happens |
|---|---|
| September 1, 2026 | MLA-C02 beta registration opens |
| September 28, 2026 | Last day to take MLA-C01 in English |
| September 29, 2026 | MLA-C02 beta exam delivery begins |
| TBD | MLA-C02 general-availability registration |
| TBD | MLA-C02 general-availability exam delivery |
The MLA-C02 beta is currently available in English only. MLA-C01 will continue to be offered in Japanese, Korean, and Simplified Chinese until MLA-C02 reaches general availability in those languages.
Candidates who earn the certification through MLA-C01 do not lose their credential when the exam changes. The certification remains valid for its normal three-year validity period.
What Is New in MLA-C02?
AWS has kept the existing four-domain framework, but the task statements and skills within those domains have been updated substantially.
The most important additions are:
Generative AI and Amazon Bedrock
MLA-C02 expands beyond conventional machine learning workloads and introduces skills related to building and operating generative AI solutions with Amazon Bedrock.
Candidates should expect greater emphasis on selecting and using foundation models, integrating GenAI capabilities into applications, and operating these workloads in production.
Retrieval-Augmented Generation
RAG architectures are now part of the skills expected from AWS machine learning engineers. Candidates should understand how enterprise data can be retrieved and supplied to foundation models to produce more relevant and context-aware responses.
Related concepts may include:
● Embeddings
● Vector search
● Knowledge bases
● Document retrieval
● Data preparation for GenAI
● Model grounding
Foundation Models and LLMs
Traditional ML model development remains important, but MLA-C02 broadens model-related knowledge to include foundation models and large language models.
Candidates should be familiar with areas such as:
● Foundation-model selection
● Model customization
● Fine-tuning
● Prompt-related workflows
● Model evaluation
● Operationalizing GenAI models
This is one of the clearest differences between MLA-C01 and MLA-C02.
Agentic AI
AWS has also added agentic AI to the certification scope. Machine learning engineers increasingly need to work with AI agents that can interact with tools, data, APIs, and other services to complete multistep tasks.
MLA-C02 reflects this change by introducing skills associated with AI-agent orchestration and complex AI workflows.
Responsible AI
Responsible AI is another area receiving greater attention. Candidates should understand how principles such as safety, security, privacy, appropriate model behavior, and responsible use apply across both traditional machine learning and generative AI workloads.
MLA-C02 vs MLA-C01: Key Differences
| Detail | MLA-C02 | MLA-C01 |
|---|---|---|
| Exam status | Updated exam – currently beta | Current version being replaced |
| Beta price | $75 USD | $150 USD standard exam |
| Main technology focus | Traditional ML + GenAI | Primarily traditional ML and MLOps |
| Amazon Bedrock | Significant new focus | Limited compared with MLA-C02 |
| RAG | Included | Not a major exam focus |
| Foundation models / LLMs | Included | Limited |
| Agentic AI | Included | Not a major focus |
| Responsible AI | Expanded | Less prominent |
| Typical roles | ML engineer, MLOps engineer, LLMOps engineer, data engineer, software developer, data scientist | Backend developer, DevOps engineer, data engineer, MLOps engineer, data scientist |
| Beta language | English | English, Japanese, Korean, Simplified Chinese* |
| Certification validity | 3 years | 3 years |
*MLA-C01 English testing ends September 28, 2026. Other supported MLA-C01 languages remain available until MLA-C02 general availability.
MLA-C02 Still Tests Core Machine Learning Engineering Skills
Candidates should not interpret the GenAI additions as meaning that traditional machine learning is disappearing from the exam. The certification continues to validate the ability to put ML workloads into production and operate them effectively.
Core skills such as the following remain important:
● Preparing data for machine learning
● Developing and evaluating ML models
● Deploying models
● Building ML workflows
● Implementing automation
● Monitoring production workloads
● Troubleshooting ML systems
● Securing ML environments
● Optimizing reliability and performance
The difference is that MLA-C02 extends these engineering practices to modern AI systems as well as traditional ML solutions.
Who Is MLA-C02 Designed For?
AWS recommends the certification for candidates with approximately one year of experience in machine learning engineering or a related field, together with hands-on experience using AWS services.
The updated exam is especially relevant to professionals working as:
● Machine Learning Engineers
● MLOps Engineers
● LLMOps Engineers
● Data Engineers
● Software Developers
● Data Scientists
The addition of LLMOps and GenAI-related responsibilities shows how AWS sees the ML engineer role evolving beyond model training and deployment.
Should You Take MLA-C01 or MLA-C02?
For candidates currently preparing for the certification, this is probably the most important question.
| Your Situation | Better Choice |
|---|---|
| You are already nearly ready for MLA-C01 | Take MLA-C01 before September 28, 2026 |
| You have only recently started studying | Consider moving to MLA-C02 |
| Your work involves Bedrock, RAG, LLMs, or GenAI | MLA-C02 is more relevant |
| You want to take the lower-cost beta exam | Consider the MLA-C02 beta |
| You want an established exam rather than a beta | Complete MLA-C01 before retirement or wait for MLA-C02 GA |
Candidates who have already invested significant time in MLA-C01 preparation do not necessarily need to switch versions. The existing exam still earns the same AWS Certified Machine Learning Engineer – Associate certification.
However, candidates starting now should pay close attention to MLA-C02 because its content better reflects the direction of AWS machine learning and AI services.
How Should Candidates Prepare for MLA-C02?
The best preparation strategy is to divide the exam into two layers.
First, make sure your traditional machine learning engineering fundamentals remain strong, particularly data preparation, model development, deployment, MLOps, monitoring, and security.
Then expand your preparation into the new MLA-C02 areas:
● Amazon Bedrock
● Generative AI
● Foundation models
● Large language models
● RAG
● Embeddings and vector search
● AI agents and agentic workflows
● Responsible AI
● GenAI operationalization
Because these areas represent some of the biggest differences from MLA-C01, candidates moving from older MLA-C01 study materials should make sure their preparation has been updated specifically for the MLA-C02 exam objectives.
For targeted preparation, candidates can review Passcert MLA-C02 practice tests alongside the official AWS exam guide to identify weak areas and become more familiar with the new Machine Learning Engineer – Associate knowledge requirements.
MLA-C02 Reflects the Changing Role of AWS Machine Learning Engineers
The move from MLA-C01 to MLA-C02 is more significant than a routine exam refresh.
AWS is effectively expanding the definition of a machine learning engineer. Traditional ML development, deployment, monitoring, and MLOps remain at the center of the certification, but candidates are now also expected to understand how foundation models, generative AI, RAG systems, Amazon Bedrock, and AI agents operate in real AWS environments.
For existing MLA-C01 candidates, the most important date is September 28, 2026, the final day to take the English version.
For candidates beginning their AWS machine learning certification journey now, MLA-C02 is the version to watch, particularly if their career goals involve modern ML engineering, generative AI, or production AI systems.
FAQs About the MLA-C02 Update
Is MLA-C02 replacing MLA-C01?
Yes. MLA-C02 is the updated version of the AWS Certified Machine Learning Engineer – Associate exam. MLA-C01 in English is available through September 28, 2026.
When does the MLA-C02 beta begin?
Registration opened September 1, 2026, and beta exam delivery begins September 29, 2026.
What are the biggest new topics in MLA-C02?
The most important additions include Amazon Bedrock, generative AI, RAG, foundation models, LLMs, agentic AI, and responsible AI.
Does MLA-C02 add new exam domains?
No. AWS keeps the existing four-domain structure but updates the task statements and skills covered within those domains.
Should current MLA-C01 candidates switch to MLA-C02?
Not necessarily. Candidates who are already well prepared for MLA-C01 can take the English exam through September 28, 2026. Candidates who are just starting may benefit from preparing directly for MLA-C02.
