Exam Prep: AWS Certified Machine Learning Engineer—Associate
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Machine Learning
On-Site, Virtual
1 day
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 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 (MLA-C01) exam.
Course objectives
- 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.
- Suggested 1 year of experience in a related role such as a backend software developer, DevOps
developer, data engineer, or data scientist.
Intended Audience
This course is intended for individuals who are preparing for the AWS Certified Machine Learning
Engineer - Associate (MLA-C01) exam.
Course outline
- 1.1 Ingest and store data.
- 1.2 Transform data and perform feature engineering.
- 1.3 Ensure data integrity and prepare data for modeling.
- 2.1 Choose a modeling approach.
- 2.2 Train and refine models.
- 2.3 Analyze model performance.
- 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.
- 4.1 Monitor model interference.
- 4.2 Monitor and optimize infrastructure costs.
- 4.3 Secure AWS resources.
