AWS Certified Machine Learning Engineer - Associate (MLA-C02)

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).

Topics

• Introduction

• Target candidate description

• Exam content

• Content outline

• Content Domain 1: Data Preparation for ML and AI

• Content Domain 2: ML Model and Foundation Model (FM) Development

• Content Domain 3: Deployment and Orchestration of ML and AI Workflows

• Content Domain 4: Operating, Monitoring, and Securing ML and AI Solutions

• Mentions of AWS services on the exam

• In-scope AWS services and features

• Out-of-scope AWS services and features

• Comparison of MLA-C01 and MLA-C02

• Survey

Introduction

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.

Target candidate description

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 general IT knowledge

The target candidate should have the following general IT knowledge:

• Understanding of common ML algorithms and their use cases

• Understanding of FM capabilities, limitations, and common 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 CI/CD pipelines and infrastructure as code (IaC)

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

Job tasks that are out of scope for the target candidate

The following list contains job tasks that the target candidate is not expected to be able to perform. This list is non-exhaustive. The following tasks are out of scope for the exam:

• Designing and architecting full end-to-end AI and ML solutions

• Setting up best practices and guiding ML strategies

• Handling integration with a wide array of services or new tools and technologies

• Working deeply in two or more ML domains

Exam content

Question types

The exam contains one or more of the following question types:

• Multiple choice: Has one correct response and three incorrect responses (distractors).

• Multiple response: Has two or more correct responses out of five or more response options. You must select all the correct responses to receive credit for the question.

Unanswered questions are scored as incorrect. There is no penalty for guessing. The exam includes 50 questions that affect your score.

Unscored content

The exam includes 15 unscored questions that do not affect your score. AWS collects information about performance on these unscored questions to evaluate them for future use as scored questions. The unscored questions are not identified on the exam.

Exam results

The AWS Certified Machine Learning Engineer - Associate (MLA-C02) exam has a pass or fail designation.¹ The exam is scored against a minimum standard established by AWS professionals who follow certification industry best practices and guidelines.

Note

¹ Does not apply to the beta version of the exam. You can find more information about beta exams in general on the AWS Certification website.

Your results for the exam are reported as a scaled score of 100–1,000. The minimum passing score is 720. Your score shows how you performed on the exam as a whole and whether you passed. Scaled scoring models help equate scores across multiple exam forms that might have slightly different difficulty levels.

Your score report could contain a table of classifications of your performance at each section level. The exam uses a compensatory scoring model, which means that you do not need to achieve a passing score in each section. You need to pass only the overall exam.

Each section of the exam has a specific weighting, so some sections have more questions than other sections have. The table of classifications contains general information that highlights your strengths and weaknesses. Use caution when you interpret section-level feedback.

Content outline

This exam guide includes weightings, content domains, tasks, and skills for the exam. This guide does not provide a comprehensive list of the content on the exam.

The exam has the following content domains and weightings:

• 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)

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