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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

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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.

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Course details

  • Level: Intermediate
  • Type: Classroom (virtual and in person)
  • Length: 1 day

  • 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.

This course is intended for individuals who are preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam.

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.

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.