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AWS’s machine learning engineering exam has moved from MLA-C01 to MLA-C02. The change is substantial but not a restructuring: MLA-C02 keeps the same four content domains and adds generative AI and foundation-model (FM) work inside all four of them. Amazon Bedrock is now named explicitly in the exam guide’s skill statements, and two domain weights shift by two percentage points. Everything else in the scored outline stays where it was.
What stayed the same and what moved
AWS’s July 2026 Training and Certification blog post put the structural point plainly. Author Vandit Kothari wrote: “The domain structure of the exam remains the same. No new domains were added.” So there is no separate GenAI domain. AI-related tasks are distributed across the existing four domains.
| Domain | MLA-C01 weight | MLA-C02 weight | What the scope now adds |
|---|---|---|---|
| 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. |
| Model development | 26% | 24% (down 2 points) | Adds foundation-model selection and customization, prompt engineering, RAG, and evaluation of AI systems. |
| Deployment and orchestration | 22% | 24% (up 2 points) | Adds FM hosting, agents, Amazon Bedrock knowledge bases, retrieval pipelines, AI-specific pipelines, and prompt and agent versioning. |
| Operations, monitoring, and security | 24% | 24% | Adds AI and agent observability, FM and token cost considerations, and AI-specific safeguards. |
The weights describe scored content, not an exact count of questions on your exam. AWS states that its guide is not a comprehensive list of everything the exam can test, so treat the task statements as the study map rather than a complete inventory.
Where Amazon Bedrock appears
Bedrock is not a single product-recognition item. AWS’s task statements place it across the ML lifecycle, so you will meet it in design, evaluation, deployment, and operations questions alike.
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Data preparation
- Prepare documents for RAG through chunking and metadata extraction.
- Work with embeddings, several input modalities, and vector storage.
Model selection and customization
- Select Bedrock foundation models against task requirements.
- Identify fine-tuning approaches and compare managed, pretrained, custom, and FM options.
- Choose a RAG architecture pattern, and decide when prompt engineering is enough versus when fine-tuning is needed.
Evaluation
- Run reproducible experiments and assess output and content quality.
- Use human evaluation, consider NLP metrics and bias, and measure retrieval accuracy.
Deployment and orchestration
- Configure FM deployment and hosting, and build agents on Bedrock knowledge bases.
- Design retrieval and reranking, manage prompts, version agents, and maintain refresh pipelines.
Operations and security
- Monitor model and agent performance, tool failures, and agent coordination.
- Track FM inference costs, plus token and embedding costs.
- Manage credentials and data protection, and apply safeguards such as Bedrock Guardrails.
The traditional ML core is still tested
MLA-C02 adds to the ML lifecycle rather than replacing it. Candidates still need to ingest and validate data, transform and engineer features, select, train, tune, and evaluate models, deploy endpoints and workloads, automate pipelines, monitor performance and drift, control costs, and secure AWS resources. Domain 1 remains the largest at 28%, and the other three domains each carry 24%, so traditional data engineering, model development, deployment, monitoring, and security deserve study time alongside the GenAI material.
Who the exam is for, and what it does not cover
AWS describes the target candidate as someone with at least one year of hands-on experience with SageMaker AI, Bedrock, and other AWS services for ML engineering, and at least one year in a related role such as backend development, DevOps, data engineering, or data science. The candidate is expected to have experience with both traditional ML and GenAI. The guide also expects working knowledge of data engineering, CI/CD, cloud monitoring, and AWS security.
The exam is relevant to ML and MLOps engineers, LLMOps practitioners, data engineers, software developers integrating ML or GenAI features, data scientists moving toward engineering work, and ML or solutions architects. AWS’s exam guide states that the exam “validates ML engineering skills and the ability to work with traditional ML models and foundation models (FMs).” It is still an associate-level credential. AWS places full end-to-end solution architecture and broad ML strategy outside the target candidate’s expected tasks, so the exam should not be read as proof of unrestricted architecture expertise.
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As of October 7, 2026, AWS lists MLA-C02 as the updated beta exam. The figures below come from AWS’s certification page and its September 2026 announcement. Confirm them on AWS Certification before you book, because beta logistics change.
| Item | Status as stated by AWS |
|---|---|
| MLA-C02 beta delivery | Began September 29, 2026 |
| Format | English only; 85 questions; 170 minutes |
| Scoring | AWS’s guide says 50 questions affect the score and 15 are unscored. Beta exams may handle results differently. |
| Delivery | Pearson VUE test center or online proctoring |
| Beta price | $75 USD, a beta-period offer |
| MLA-C02 general availability | January 14, 2027 |
| MLA-C01 English testing | Ended September 28, 2026 |
| MLA-C01 in Japanese, Korean, and Simplified Chinese | Still available during the beta period, until the general-availability transition |
| MLA-C01 retirement | Announced for all languages with the January 14, 2027 transition |
| Credential validity | Three years, per the AWS Certification page |
In practical terms, English-speaking candidates can currently sit only the MLA-C02 beta, while candidates who need Japanese, Korean, or Simplified Chinese can still sit MLA-C01 until the transition. The beta price and format describe the current offer and schedule, not a permanent promise.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to prepare
- Refresh traditional ML fluency. Cover data handling, model selection, training, tuning, evaluation, deployment, monitoring, and security on AWS.
- Learn foundation-model basics on Bedrock. Practice model choice, prompt work, the difference between customization and fine-tuning, evaluation, deployment, and cost tradeoffs.
- Build RAG fluency. Work through embeddings, vector storage, chunking, retrieval, reranking, document refresh, and retrieval evaluation.
- Study operational AI. Cover agent deployment and monitoring, workflow orchestration, versioning, safeguards, and cost management.
- Use AWS’s official preparation resources. AWS points candidates to its Skill Builder exam-preparation plan, official practice questions, a pretest, and a practice exam.
- Book only after checking the scheduling status above. Language availability and beta handling are the items most likely to change before your date.
If you already work with traditional ML on AWS, the most useful gap to close is usually the Bedrock and RAG material in the deployment and evaluation domains, since those areas carry the largest scope change.
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