Exam Prep: AWS Certified Machine Learning Engineer – Associate (MLA-C01)
Learn to build, deploy, and maintain ML solutions while gaining confidence for the MLA-C01 certification exam
Exam Prep: AWS Certified Machine Learning Engineer Associate (MLA-C01)
Accelerate your journey toward the AWS Certified Machine Learning Engineer - Associate certification with this focused one-day preparation course. Through interactive lectures, practice questions, and real-world case studies, you’ll gain an understanding of the key exam domains, including ML model development, deployment, and operations on AWS. The course is designed to help you:
- Identify your unique strengths and knowledge gaps related to the exam objectives
- Develop a targeted study plan to shore up areas that need more attention
- Gain exam-taking tips and test-taking strategies to maximize your performance
By the end of the course, you’ll have a clear roadmap for completing your preparation and feeling confident to take the certification exam.

Course details
Course overview
- Level: Intermediate
- Type: Classroom (virtual and in person)
- Length: 1 day
What you'll learn
- Identify the scope and content tested by the AWS Certified Machine Learning Engineer - Associate (MLAC01) exam.
- Practice exam-style questions and evaluate your preparation strategy.
- Examine use cases and differentiate between them.
Who should take this course
This course is intended for individuals who are preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam.
What experience you'll need
You are not required to take any specific training before taking this course. However, the following prerequisite knowledge is recommended prior to taking the AWS Certified Machine Learning Engineer - Associate (MLAC01) exam.
General IT knowledge
Learners are recommended to have the following:
- Suggested 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist.
- Basic understanding of common ML algorithms and their use cases
- Data engineering fundamentals, including knowledge of common data formats, ingestion, and transformation to work with ML data pipelines
- Knowledge of querying and transforming data
- Knowledge of software engineering best practices for modular, reusable code development, deployment, and debugging
- Familiarity with provisioning and monitoring cloud and on-premises ML resources
- Experience with continuous integration and continuous delivery (CI/CD) pipelines and infrastructure as code (IaC)
- Experience with code repositories for version control and CI/CD pipelines
Recommended AWS knowledge
Learners are recommended to be able to do the following:
- Suggested 1 year of experience using Amazon SageMaker AI and other AWS services for ML engineering.
- Knowledge of Amazon SageMaker AI 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.
What languages are offered
This course is offered in the following languages: English.
We regularly update our courses based on customer feedback and AWS service updates. As a result, course content may vary between languages while we localize these updates.