Associate MLA-C01
Validates skills to build, deploy, and maintain ML solutions on AWS using SageMaker, Bedrock, and MLOps best practices.
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The AWS Certified Machine Learning Engineer – Associate (MLA-C01) was introduced in 2024 to replace the retiring Machine Learning Specialty. It targets ML engineers who operationalize and deploy machine learning models in production environments on AWS, rather than data scientists who focus on model development.
The exam covers four domains: Data Preparation for Machine Learning (28%), ML Model Development (26%), Deployment and Orchestration of ML Workflows (22%), and ML Solution Monitoring, Maintenance, and Security (24%). Core services include Amazon SageMaker (including SageMaker Pipelines, Model Monitor, and Feature Store), Amazon Bedrock, AWS Glue, S3, IAM, and CloudWatch. The exam emphasizes MLOps: automating training, deploying endpoints, handling model drift, and securing ML pipelines.
This certification has 65 questions in 130 minutes with a passing score of 720/1000. It is the right choice for ML engineers, data engineers with ML responsibilities, and DevOps engineers supporting ML teams. It sits at the Associate level, making it more accessible than the old Specialty while still demonstrating deep ML engineering competence.
“SageMaker, feature engineering, model evaluation — all covered properly. Watching my score climb between mocks kept me at it. Passed first try.”
“I'd stalled on MLA-C01 until I found this. Realistic questions, clear explanations, and I could go back to the topics that kept catching me out.”
Every MLA-C01 question is written by someone who holds this certification and works in the field — not scraped from other sites, and not generated and shipped unreviewed. Every explanation you are shown has been read and approved by that same expert first.
ML engineers, data engineers, and DevOps engineers who build, deploy, and maintain ML models in production on AWS.
Prerequisites: 1+ year of experience with ML workloads on AWS. Familiarity with Python, SageMaker, and basic ML concepts.