AWS MLA-C01 exam guide covering ML data prep, model development, deployment, operations, and security decisions.
This guide targets AWS Certified Machine Learning Engineer - Associate (MLA-C01), AWS’s current associate-level certification for people who build, operationalize, deploy, and maintain ML solutions and pipelines on AWS. The exam rewards practical operational judgment about data preparation, model choice, SageMaker workflows, deployment targets, observability, and the controls that keep ML systems reliable after launch.
MLOps: The deployment, monitoring, versioning, and lifecycle discipline that keeps machine-learning systems reliable after training.
Feature Store: Managed place to store and serve engineered features consistently for training and inference workflows.
At a glance
Exam fact
Current official value
Level
Associate
Duration
130 minutes
Format
65 questions across multiple-choice, multiple-response, ordering, and matching items
Passing score
720 scaled score
Validity
3 years
Typical next step
deeper MLOps, SageMaker, or domain-specific AWS ML work after the associate operational baseline is solid
AWS currently positions MLA-C01 for people with at least one year of experience using Amazon SageMaker and related AWS ML services. The current exam guide breaks the scored content into four weighted domains, and this online guide now follows that structure directly:
separate training, deployment, monitoring, and governance as distinct lanes
choose the SageMaker or AWS service that matches the operational constraint, not just the model type
balance latency, cost, scaling, and rollback safety when choosing inference patterns
treat security, network placement, encryption, and observability as core parts of the solution
Review flow
flowchart LR
A["Data prep and feature handling"] --> B["Training and evaluation"]
B --> C["Registry, pipelines, and deployment"]
C --> D["Monitoring, drift, and rollback"]
D --> E["Security and cost controls"]
Best fit for this guide
If you are coming from…
Bias your review toward…
data science
deployment, monitoring, and MLOps controls
DevOps or platform engineering
SageMaker service fit, model lifecycle, and ML-specific monitoring