MLA-C02 keeps the same four scored domains and the same ML-engineering lifecycle as MLA-C01. What changed is scope. AWS now places foundation models (FMs), generative AI, retrieval-augmented generation (RAG), agents, and AI evaluation inside all four domains, and Amazon Bedrock is named directly in the exam guide and its skill statements. AWS did not add a fifth “GenAI” domain. As of October 7, 2026, MLA-C02 is in beta, with delivery beginning September 29, 2026.
What moved in the scored outline
The clearest way to see the change is to compare domain weights and the new scope inside each domain. Weights describe scored content, not an exact count of questions.
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| Domain | MLA-C01 weight | MLA-C02 weight | What the MLA-C02 scope adds |
|---|---|---|---|
| 1. Data preparation | 28% | 28% | Extends from ML data preparation to ML and AI data: embeddings, multimodal inputs, vector databases, RAG document preparation, and FM training data. |
| 2. Model development | 26% | 24% | Explicitly covers foundation-model selection and customization, prompt engineering, RAG, and evaluation of AI systems. The weight drops two points while the scope grows. |
| 3. Deployment and orchestration | 22% | 24% | Adds FM hosting, agents, Bedrock knowledge bases, retrieval pipelines, AI-specific pipelines, and prompt and agent versioning. |
| 4. Operations, monitoring, and security | 24% | 24% | Adds AI and agent observability, FM and token cost considerations, and AI-specific safeguards. |
Only the two middle domains changed weight: model development moved from 26% to 24%, and deployment and orchestration moved from 22% to 24%. Data preparation and operations, monitoring, and security kept their weights. AWS also states that the exam guide is not a comprehensive list of all exam content, so the table tells you where emphasis sits, not every topic that can appear.
AWS Training and Certification author Vandit Kothari put the structural point plainly in a July 14, 2026 blog post: “The domain structure of the exam remains the same. No new domains were added.”
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What “Bedrock is in” means in practice
Bedrock is not confined to a product-name question. AWS’s task outlines place it across the whole lifecycle, which is why the change matters more than a single service added to a list.
Data preparation
Candidates are expected to prepare RAG documents through chunking and metadata extraction, and to work with embeddings, several input modalities, and vector storage.
Choosing and customizing a model
The exam expects you to select a Bedrock foundation model against task requirements, identify fine-tuning approaches, and compare managed, pretrained, custom, and FM approaches. You also need to choose a RAG architecture pattern and decide when prompt engineering is enough and when fine-tuning is justified.
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Expect reproducible experiments, evaluation of outputs and content quality, human evaluation, NLP metrics, and bias assessment. Retrieval accuracy is part of the same picture, so evaluating a RAG pipeline means measuring what it retrieves as well as what it generates.
Deployment and orchestration
This domain covers FM deployment and hosting, agents, Bedrock knowledge bases, retrieval and reranking, prompt management, agent versioning, and refresh pipelines for the documents behind a RAG system.
Operations and security
Monitoring now includes model and agent performance, tool failures and coordination between agents, FM inference costs, and token and embedding costs. Security covers credentials, data protection, and safeguards such as Bedrock Guardrails.
What the exam does not claim to be
MLA-C02 is still an associate-level certification. AWS’s own target-candidate description calls for hands-on familiarity with both traditional ML and GenAI, but it places full end-to-end solution architecture and broad ML strategy outside the expected tasks. The guide’s stated validation goal is “ML engineering skills and the ability to work with traditional ML models and foundation models (FMs).” Treat the exam as a test of working engineering skill across both areas, not as a specialist examination of every AI and ML discipline.
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The traditional ML core has not gone away
The GenAI material is layered onto the existing lifecycle rather than replacing it. Candidates still need to:
- Ingest and validate data, and transform and engineer features.
- Select, train, tune, and evaluate models.
- Deploy endpoints and workloads, and automate pipelines.
- Monitor performance and drift, and control costs.
- Secure AWS resources, including CI/CD and cloud monitoring practices.
Data preparation is still the largest weighted domain, so traditional data engineering deserves as much study time as the new FM material.
Beta logistics and the MLA-C01 transition
Scheduling details change quickly, so the dates below come from AWS’s certification pages and its September 2026 announcement, as accessed October 7, 2026.
| Date | What AWS states |
|---|---|
| September 28, 2026 | English testing for MLA-C01 ends. |
| September 29, 2026 | MLA-C02 beta delivery begins. |
| September 29, 2026 to January 14, 2027 | MLA-C01 remains available in Japanese, Korean, and Simplified Chinese during the beta period. |
| January 14, 2027 | MLA-C02 general-availability delivery is scheduled to begin, and MLA-C01 retires in all languages. |
The current MLA-C02 beta details on AWS’s certification page are:
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- Length: 85 questions, 170 minutes.
- Delivery: Pearson VUE test center or online proctoring.
- Price: $75 USD, which AWS describes as beta pricing.
- Scoring: 50 questions affect the score and 15 are unscored. AWS notes that beta exams may handle results differently, so check the latest beta terms before booking.
- Validity: the certification page lists the credential as valid for three years.
The beta price and schedule describe what AWS offered at the time of access. They are not a permanent commitment, and they may change before general availability.
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Who should consider MLA-C02
AWS describes the intended candidate as someone with at least one year of hands-on work using SageMaker AI, Bedrock, and other AWS services for ML engineering. That candidate should also have at least a year in a related role such as backend development, DevOps, data engineering, or data science, and experience with both traditional ML and GenAI.
The exam is relevant to several roles:
- ML and MLOps engineers.
- LLMOps practitioners.
- Data engineers building ML or GenAI data pipelines.
- Software developers integrating ML or GenAI features.
- Data scientists moving toward engineering responsibilities.
- ML and solutions architects.
Listing a role does not mean the certification validates architecture expertise across the whole field.
A practical study sequence
Compare your current readiness with the work you want to do, then study in this order:
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems- Traditional ML fluency. Data handling, model selection, training, tuning, evaluation, deployment, monitoring, and security.
- Foundation-model familiarity. Bedrock model choice, prompt work, customization and fine-tuning concepts, evaluation, deployment, and cost tradeoffs.
- RAG and data readiness. Embeddings, vector storage, chunking, retrieval, reranking, document refresh, and retrieval evaluation.
- Operational AI. Agent deployment and monitoring, workflow orchestration, versioning, safeguards, and cost management.
For preparation, AWS points candidates to its Skill Builder exam-preparation plan, official practice questions, a pretest, and a practice exam. These are the official digital resources AWS names for this exam.
If you already work with traditional ML on AWS, start with the RAG and agent material, since it is the part most likely to be new. If your background is mainly generative AI, spend extra time on training pipelines, model monitoring, and deployment controls.
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