AWS Certified Machine Learning Engineer-Associate (MLA-C01) Exam - Exam Overview

Sep 20, 2026

AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam training outlines the abilities required to build, operationalize, deploy, and maintain machine learning solutions and pipelines using AWS Cloud. It covers data ingestion, transformation, validation, and preparation; model selection, training, hyperparameter tuning, performance analysis, and version management. It also covers deployment infrastructure and endpoints, compute provisioning, auto scaling, CI/CD orchestration, continuous monitoring, and securing ML systems via access controls, compliance features, encryption, and data protection best practices.

LanguageEnglish

CategoryExam

FormatPDF

Pages2

Reading Time5 min

AccessFree to download

Training Goals

Recommended AWS Knowledge

Domains & Competencies

Domain 1: Data Preparation for Machine Learning

Domain 2: ML Model Development

Domain 3: Deployment and Orchestration of ML Workflows

Domain 4: ML Solution Monitoring, Maintenance, and Security

Exam Overview

Level, Length, Cost

Format and Delivery Method

Training: AWS AWS Certiﬁed Machine Learning Engineer-Associate (MLA-C01) Exam Training: AWS AWS Certiﬁed Machine Learning Engineer-Associate (MLA-C01) Exam TRAINING GOALS: The AWS Certiﬁed Machine Learning Engineer-Associate (MLA-C01) exam validates a candidate’s ability to build, operationalize, deploy, and maintain machine learning (ML) solutions and pipelines by using the AWS Cloud. The exam also validates a candidate’s ability to complete the following tasks: Ingest, transform, validate, and prepare data for ML modeling. Select general modeling approaches, train models, tune hyperparameters, analyze model performance, and manage model versions. Choose deployment infrastructure and endpoints, provision compute resources, and conﬁgure auto scaling based on requirements. Set up continuous integration and continuous delivery (CI/CD) pipelines to automate orchestration of ML workﬂows. Monitor models, data, and infrastructure to detect issues. Secure ML systems and resources through access controls, compliance features, and best practices. The target candidate should have at least 1 year of experience using Amazon SageMaker and other AWS services for ML engineering. The target candidate also should have at least 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist. Recommended AWS knowledge The target candidate should have the following AWS knowledge: Knowledge of SageMaker capabilities and algorithms for model building and deployment Knowledge of AWS data storage and processing services for preparing data for modeling Familiarity with deploying applications and infrastructure on AWS Knowledge of monitoring tools for logging and troubleshooting ML systems Knowledge of AWS services for the automation and orchestration of CI/CD pipelines Understanding of AWS security best practices for identity and access management, encryption, and data protection | [www.compendium.pl](www.compendium.pl) | page 1 of 2 | | --- | --- | Compendium Centrum Edukacyjne Spółka z o.o. ul. Tatarska 5, 30-103 Kraków, tel.: (12) 298 47 77 ul. Postępu 18B, 02-676 Warszawa, tel.: (22) 417 41 70 BNP Paribas Bank Polska S.A. EUR: PL 79 1600 1462 1853 6451 3000 0006 USD: PL 52 1600 1462 1853 6451 3000 0007 Training: AWS AWS Certiﬁed Machine Learning Engineer-Associate (MLA-C01) Exam Domains & Competencies Domain 1: Data Preparation for Machine Learning (ML) (28% of scored content) Domain 2: ML Model Development (26% of scored content) Domain 3: Deployment and Orchestration of ML Workﬂows (22% of scored content) Domain 4: ML Solution Monitoring, Maintenance, and Security (24% of scored content) Detailed described domains, list of speciﬁc tools and technologies that might be covered on the exam, as well as lists of in-scope AWS services [https://d1.awsstatic.com/training-and-certi](https://d1.awsstatic.com/training-and-certi)ﬁcation/docs-machine-learning-engineer-associate/AWS-Certiﬁed-Machine-Learning-Engineer-Associate_Exam-Guide.pdf Exam overview Level: Associate Length: 130 minutes to complete the exam Cost: 150 USD (*when purchasing directly from AWS) Visit Exam pricing for additional cost information. Format: 65 questions; either multiple choice or multiple response Delivery method: Pearson VUE testing center or online proctored exam. Diﬃculty level | [www.compendium.pl](www.compendium.pl) | page 2 of 2 | | --- | --- | Compendium Centrum Edukacyjne Spółka z o.o. ul. Tatarska 5, 30-103 Kraków, tel.: (12) 298 47 77 ul. Postępu 18B, 02-676 Warszawa, tel.: (22) 417 41 70 BNP Paribas Bank Polska S.A. EUR: PL 79 1600 1462 1853 6451 3000 0006 USD: PL 52 1600 1462 1853 6451 3000 0007

FAQs

What does the AWS Certified Machine Learning Engineer-Associate (MLA-C01) exam validate?

It validates the ability to build, operationalize, deploy, and maintain machine learning solutions and pipelines using the AWS Cloud, including end-to-end ML workflow tasks.

Which core skills are expected for the exam?

Candidates should prepare data for ML modeling, select and train models, tune hyperparameters, analyze performance, manage model versions, and handle deployment with endpoints and auto scaling.

What are the exam length, format, and delivery options?

The exam takes 130 minutes, includes 65 questions (multiple choice or multiple response), and can be delivered via Pearson VUE testing center or online proctored exam.

Sep 20, 2026

Uploaded by Bill Black