1-Day Instructor-Led Training
Official AWS Curriculum
After-Course Instructor-Coaching Included
#1967
Exam Prep: AWS Certified Machine Learning Engineer – Associate
Course 1967
- Duration: 1 day
- Language: English
- Level: Intermediate
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.
AWS ML Engineer Associate Exam Prep Delivery Methods
Online
Upskill your whole team by bringing Private Team Training to your facility.
AWS ML Engineer Associate Exam Prep Course Benefits
In this AWS course, you will:
- 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
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
AWS ML Engineer Associate Exam Prep Course Outline
Domain 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.
Domain 2: ML Model Development
2.1 Choose a modeling approach.
2.2 Train and refine models.
2.3 Analyze model performance.
Domain 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.
Domain 4: ML Solution Monitoring, Maintenance, and Security
4.1 Monitor model interference.
4.2 Monitor and optimize infrastructure costs.
4.3 Secure AWS resources.
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AWS ML Engineer Associate Exam Prep FAQs
AWS Certification exams are delivered in two formats: online proctored or in-person at authorized testing centers through Pearson VUE or PSI. Candidates can choose the option that best fits their schedule and location. Online proctored exams require a secure environment, webcam, and reliable internet connection.
Most AWS exams are 130 minutes long (some foundational-level exams may be shorter). AWS does not publish exact passing scores as they vary slightly for each version of the exam. However, a scaled score of 700 out of 1000 is generally required to pass.
AWS Certifications validate your cloud expertise and are recognized globally by employers. They can help professionals advance their careers, improve job prospects, and qualify for roles involving cloud architecture, development, operations, and security. Certified individuals also gain access to the AWS Certified Global Community and exclusive digital badges.