Exam Prep: AWS Certified Machine Learning Engineer [GK910030]

OVERVIEW

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.

OBJECTIVES

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.

AUDIENCE

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.

CONTENT

Module 1: Data Preparation for Machine Learning (ML)

• 1.1 Ingest and store data.
• 1.2 Transform data and perform feature engineering.
• 1.3 Ensure data integrity and prepare data for modeling

Module 2:ML Model Development

• 2.1 Choose a modeling approach.
• 2.2 Train and refine models.
• 2.3 Analyze model performance.

Module 3: Deployment and Orchestration of ML Workflows

• 3.1 Select deployment infrastructure based on existing architecture and requirements.
• 3.2 Create and script infrastructure based on existing architecture and requirements.
• 3.3 Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines

Module 4: ML Solution Monitoring, Maintenance, and Security

• 4.1 Monitor model interference.
• 4.2 Monitor and optimize infrastructure costs.
• 4.3 Secure AWS resources.