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.
Topics
Introduction
The AWS Certified Machine Learning Engineer - Associate (MLA-C01)
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.
Target Candidate Description
The target candidate should have at least 1 year of experience using Amazon SageMaker 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.
Recommended general IT knowledge
The target candidate should have the following general IT knowledge:
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 CI/CD pipelines and infrastructure as code (IaC)
Experience with code repositories for version control and CI/CD pipelines
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
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. These tasks are out of scope for the exam:
Designing and architecting full end-to-end 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 (for example, natural language processing [NLP], computer vision)
Quantizing models and analyzing the impact on accuracy
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.
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.
Unanswered questions on the exam 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 these questions for future use as scored questions. These unscored questions are not identified on the exam.
Exam results
The AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam has a pass or fail designation. The exam is scored against a minimum standard established by AWS professionals who follow certification industry best practices and guidelines.
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, and task statements for the exam. This guide does not provide a comprehensive list of the content on the exam. However, additional context for each task statement is available to help you prepare for the exam.
The exam has the following content domains and weightings:
Content Domain 1: Data Preparation for Machine Learning (ML) (28% of scored content)
Content Domain 2: ML Model Development (26% of scored content)
Content Domain 3: Deployment and Orchestration of ML Workflows (22% of scored content)
Content Domain 4: ML Solution Monitoring, Maintenance, and Security (24% of scored content)
AWS Services for the Exam
The AWS Certified Machine Learning Engineer - Associate exam covers specific AWS services that are relevant to machine learning engineers. Understanding which services are in scope can help you focus your preparation efforts.
For detailed information about the AWS services covered in the exam, see the following section:
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