Overview
Exam Readiness: AWS Certified Machine Learning Engineer – Associate (MLA-C01) is a one-day ILT where you learn how to assess your preparedness for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam. The exam validates a candidate’s ability to build, operationalize, and maintain machine learning (ML) solutions and pipelines by using the AWS Cloud.
This intermediate-level course prepares you for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) 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 exam style 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 (MLA-C01) exam.
Skills Covered
In this course, you will learn to:
- Identify the scope and content tested by the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam.
- Practice exam-style questions and evaluate your preparation strategy.
- Examine use cases and differentiate between them
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 (MLA-C01) 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
Target Audience
This course is intended for individuals who are preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam
Module 1: Data Preparation for Machine Learning (ML)
- Ingest and store data.
- Transform data and perform feature engineering.
- Ensure data integrity and prepare data for modeling.
Module 2: ML Model Development
- Choose a modeling approach.
- Train and refine models.
- Analyze model performance.
Module 3: Deployment and Orchestration of ML Workflows
- Select deployment infrastructure based on existing architecture and requirements.
- Create and script infrastructure based on existing architecture and requirements.
- Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines.
Module 4: ML Solution Monitoring, Maintenance, and Security
- Monitor model interference.
- Monitor and optimize infrastructure costs.
- Secure AWS resources
Dates & Locations
July 24, 2026 - July 24, 2026
July 24, 2026 - July 24, 2026
September 18, 2026 - September 18, 2026
September 18, 2026 - September 18, 2026
November 20, 2026 - November 20, 2026
November 20, 2026 - November 20, 2026
Exam & Certification
Exam Readiness: AWS Certified Machine Learning Engineer
Training & Certification Guide
Frequently Asked Questions
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