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

AWS Certified Machine Learning Engineer - Associate (MLA-C01) Exam Prep is a one-day, instructor-led training (ILT) course designed to help participants evaluate their readiness for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam and reinforce their understanding of the key topics covered by the certification.

The MLA-C01 certification exam focuses on a candidate's ability to build, operationalize, and maintain machine learning (ML) solutions and pipelines using the AWS Cloud.

Designed at an intermediate level, this course provides a detailed review of the major domains included in the exam. Participants explore how these topics relate to the development of artificial intelligence and machine learning solutions on AWS. Detailed explanations and walkthroughs of exam-style questions help learners reinforce existing knowledge, identify areas that require further study, and develop effective approaches for answering exam questions.

Reviewing sample questions in an exam-style format also helps participants improve their ability to distinguish between correct and incorrect responses and strengthen their overall test-taking approach. By the end of the course, learners will have a stronger understanding of the concepts and practical applications assessed in the MLA-C01 exam.

Course Activities

The course includes:

• Topic overview presentations,
• Exam-style practice questions,
• Use cases,
• Group discussions and interactive activities.

There is no mandatory training that must be completed before attending this course. However, candidates preparing for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam are recommended to have the following knowledge and experience.

Recommended General IT Knowledge

Participants are recommended to have:

• Approximately one year of experience in a related role, such as backend software developer, DevOps developer, data engineer, or data scientist, along with a basic understanding of common ML algorithms and their use cases,
• An understanding of data engineering fundamentals, including common data formats, data ingestion, and transformation processes used in ML data pipelines,
• Knowledge of querying and transforming data,
• Familiarity with software engineering best practices for developing modular and reusable code, as well as deployment and debugging,
• Experience or familiarity with provisioning and monitoring cloud-based and on-premises ML resources,
• Experience with continuous integration and continuous delivery (CI/CD) pipelines and Infrastructure as Code (IaC),
• Experience using code repositories for version control and CI/CD workflows.

Recommended AWS Knowledge

Participants are recommended to have:

• Approximately one year of experience using Amazon SageMaker AI and other AWS services for machine learning engineering,
• Knowledge of Amazon SageMaker AI features and algorithms used for model building and deployment,
• Knowledge of AWS data storage and processing services used to prepare data for modeling,
• Familiarity with deploying applications and infrastructure on AWS,
• Knowledge of monitoring tools used for logging and troubleshooting ML systems,
• Knowledge of AWS services used to automate and orchestrate CI/CD pipelines,
• An understanding of AWS security best practices, including identity and access management, encryption, and data protection.

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

By the end of this course, participants will be able to:

• Identify the scope and major content areas assessed by the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam,
• Practice exam-style questions and use the results to evaluate their preparation and test-taking strategy,
• Review different use cases and distinguish between their key requirements and characteristics.

Domain 1: Data Preparation for Machine Learning (ML)

1.1 Ingest and store data

1.2 Transform data and perform feature engineering

1.3 Maintain data integrity and prepare data for modeling

Domain 2: ML Model Development

2.1 Select an appropriate modeling approach

2.2 Train and refine models

2.3 Evaluate and analyze model performance

Domain 3: Deployment and Orchestration of ML Workflows

3.1 Select deployment infrastructure according to the existing architecture and requirements

3.2 Create and script infrastructure based on architectural and operational requirements

3.3 Use automated orchestration tools to establish continuous integration and continuous delivery (CI/CD) pipelines

Domain 4: ML Solution Monitoring, Maintenance, and Security

4.1 Monitor model inference

4.2 Monitor and optimize infrastructure costs

4.3 Secure AWS resources

Exams and Assessments

The AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification exam is not included as part of this course. The exam must be purchased and scheduled separately.