Compendium Centrum Edukacyjne

The AWS Certified Machine Learning Engineer - Associate (MLA-C02) exam validates a candidate's ability to build, operationalize, deploy, and maintain AI and ML solutions and pipelines by using the AWS Cloud. The exam validates ML engineering skills and the ability to work with traditional ML models and foundation models (FMs).

The exam also validates a candidate’s ability to complete the following tasks:

• Ingest, transform, validate, and prepare data for AI and ML modeling.
• Select general modeling approaches, train models, tune hyperparameters, analyze model performance, and manage model versions.
• Choose deployment infrastructure and endpoints, provision compute resources, and configure auto scaling based on requirements.
• Set up continuous integration and continuous delivery (CI/CD) pipelines to automate the orchestration of AI and ML workflows.
• Build agentic workflows and maintain observability to optimize efficiency and cost.
• Monitor models, agentic workflows, data, and infrastructure to detect issues.
• Secure AI and ML systems and resources through access controls, compliance features, and best practices.

The target candidate should have at least 1 year of experience using Amazon SageMaker AI, Amazon Bedrock, and other AWS services for ML engineering. The target candidate also should have at least 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist. The candidate should have experience with both traditional ML and generative AI (GenAI).

Recommended AWS knowledge

The target candidate should have the following AWS knowledge:

• Knowledge of SageMaker AI capabilities and algorithms for both traditional ML models and GenAI models
• Knowledge of Amazon Bedrock features and capabilities
• Knowledge of AWS data storage and data processing services to prepare 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

Domains & Competencies

Content Domain 1: Data Preparation for ML and AI (28% of scored content)

Content Domain 2: ML Model and Foundation Model (FM) Development (24% of scored content)

Content Domain 3: Deployment and Orchestration of ML and AI Workflows (24% of scored content)

Content Domain 4: Operating, Monitoring, and Securing ML and AI Solutions (24% of scored content)

Detailed described domains, list of specific tools and technologies that might be covered on the exam, as well as lists of in-scope AWS services https://docs.aws.amazon.com/pdfs/aws-certification/latest/machine-learning-engineer-associate-02/machine-learning-engineer-associate-02.pdf#machine-learning-engineer-associate-02

Exam overview

Level: Associate

Length: 170 minutes to complete the exam

Cost: 150 USD (*when purchasing directly from AWS)

Visit Exam pricing for additional cost information.

Format: 85 questions; either multiple choice or multiple response

Delivery method: Pearson VUE testing center or online proctored exam.