Exam Prep: AWS Certified Machine Learning Engineer – Associate

Please find our upcoming course dates at the end of this page!

Course description

This intermediate-level course prepares you for the AWS Certified Machine Learning Engineer – Associate exam by providing a comprehensive exploration of the exam topics. You’ll delve into the key areas covered on the exam, understanding how they relate to developing AI and machine learning solutions on the AWS platform. Through detailed explanations and walkthroughs of examstyle questions, you’ll reinforce your knowledge, identify gaps in your understanding, and gain valuable strategies for tackling questions effectively. The course includes review of exam-style sample questions, to help you recognize incorrect responses and hone your test-taking abilities. By the end, you’ll have a firm grasp on the concepts and practical applications tested on the AWS Certified Machine Learning Engineer – Associate exam.

COURSE OBJECTIVES

In this course, you will learn to:

• Identify the scope and content tested by the AWS Certified Machine Learning Engineer – Associate exam.
• Practice exam-style questions and evaluate your preparation strategy.
• Examine use cases and differentiate between them.

INTENDED AUDIENCE

This course is intended for:

• individuals who are preparing for the AWS Certified Machine Learning
  Engineer – Associate exam

Prerequisites

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 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

ACTIVITIES

This course includes:

• Subject overview presentations
• Exam-style questions
• Use cases
• Group discussions and activities

COURSE DURATION / PRICE

• 1 day
• € 775.00 (excl. tax) per person (DE)

Course outline

IMPORTANT: Please bring your notebook (Windows, Linux or Mac) to our trainings. If this is not possible, please contact us in advance.

Course materials are in English, on request also in German (if available).
The Course language is German, on request also in English.