AWS Certified Machine Learning Engineer - Associate (MLA-C01)
The AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam validates a candidate’s ability to build, operationalize, deploy, and maintain machine learning (ML) solutions and pipelines by using the AWS Cloud.
The exam also validates a candidate’s ability to complete the following tasks:
- Ingest, transform, validate, and prepare data for 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 orchestration of ML workflows.
- Monitor models, data, and infrastructure to detect issues.
- Secure ML systems and resources through access controls, compliance features, and best practices.
All Courses Idea
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.
- Ordering: Has a list of 3–5 responses to complete a specified task. You must select the correct responses and place the responses in the correct order to receive credit for the question.
- Matching: Has a list of responses to match with a list of 3–7 prompts. You must match all the pairs correctly to receive credit for the question.
- Case study: Has one scenario with two or more questions about the scenario. The scenario is the same for each question in the case study.
Unanswered questions on the exam are scored as incorrect. The exam includes 50 questions that affect your score. The exam includes 15 unscored questions that do not affect your score. These unscored questions are not identified on the exam.
Exam results:
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. 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.
Content outline:
- Domain 1: Data Preparation for Machine Learning (ML) (28% of scored content)
- Domain 2: ML Model Development (26% of scored content)
- Domain 3: Deployment and Orchestration of ML Workflows (22% of scored content)
- Domain 4: ML Solution Monitoring, Maintenance, and Security (24% of scored content)
Click here for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) Exam Content
Recommended AWS knowledge
The target candidate should have the following AWS knowledge:
- Knowledge of SageMaker 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
