AWS Certified Machine Learning - Specialty (MLS-C01) Exam Guide

AWS Certified Machine Learning - Specialty (MLS-C01) Exam Guide

This guide provides a comprehensive overview of the AWS Certified Machine Learning - Specialty (MLS-C01) exam. It includes details on the exam's structure, content domains, task statements, and the relevant AWS services and features. It also includes an appendix with additional resources.


Introduction #

The AWS Certified Machine Learning - Specialty (MLS-C01) exam is intended for individuals who perform an artificial intelligence/machine learning (AI/ML) development or data science role. The exam validates a candidate’s ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud.
The exam also validates a candidate’s ability to complete the following tasks:

Target candidate description #

The target candidate should have 2 or more years of experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud.

The target candidate should have the following AWS knowledge:

Knowledge that is out of scope for the target candidate #

The following list contains knowledge that the target candidate is not expected to have. This list is non-exhaustive. Knowledge in the following areas is out of scope for the exam:

Refer to the Appendix for a list of technologies and concepts that might appear on the exam, a list of in-scope AWS services and features, and a list of out-of-scope AWS services and features.

Exam content #

Response types #

There are two types of questions on the exam:

Select one or more responses that best complete the statement or answer the question. Distractors, or incorrect answers, are response options that a candidate with incomplete knowledge or skill might choose. Distractors are generally plausible responses that match the content area. 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 these questions for future use as scored questions. These unscored questions are not identified on the exam.

Exam results #

The AWS Certified Machine Learning - Specialty (MLS-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 750. 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:

Domain 1: Data Engineering #

Task Statement 1.1: Create data repositories for ML.

Task Statement 1.2: Identify and implement a data ingestion solution.

Task Statement 1.3: Identify and implement a data transformation solution.

Domain 2: Exploratory Data Analysis #

Task Statement 2.1: Sanitize and prepare data for modeling.

Task Statement 2.2: Perform feature engineering.

Task Statement 2.3: Analyze and visualize data for ML.

Domain 3: Modeling #

Task Statement 3.1: Frame business problems as ML problems.

Task Statement 3.2: Select the appropriate model(s) for a given ML problem.

Task Statement 3.3: Train ML models.

Task Statement 3.4: Perform hyperparameter optimization.

Task Statement 3.5: Evaluate ML models.

Domain 4: Machine Learning Implementation and Operations #

Task Statement 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance.

Task Statement 4.2: Recommend and implement the appropriate ML services and features for a given problem.

Task Statement 4.3: Apply basic AWS security practices to ML solutions.

Task Statement 4.4: Deploy and operationalize ML solutions.

Appendix #

Technologies and concepts that might appear on the exam #

The following list contains technologies and concepts that might appear on the exam. This list is non-exhaustive and is subject to change. The order and placement of the items in this list is no indication of their relative weight or importance on the exam:

In-scope AWS services and features #

The following list contains AWS services and features that are in scope for the exam. This list is non-exhaustive and is subject to change. AWS offerings appear in categories that align with the offerings’ primary functions:

Analytics:

Compute:

Containers:

Database:

Internet of Things:

Machine Learning:

Management and Governance:

Networking and Content Delivery:

Security, Identity, and Compliance:

Storage:

Out-of-scope AWS services and features #

The following list contains AWS services and features that are out of scope for the exam. This list is non-exhaustive and is subject to change. AWS offerings that are entirely unrelated to the target job roles for the exam are excluded from this list: Out-of-scope AWS services and features include the following:

Analytics:

Machine Learning: