Exam Prep: 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 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 level:
Intermediate
Duration: 1
day
Activities
This course includes
subject overview presentations, exam-style questions, use cases, and
group discussions and activities.
Course
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.
Intended Audience
This
course is intended for individuals who are preparing for the AWS Certified
Machine Learning Engineer - Associate (MLA-C01)
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 (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
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
