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  1. Programs
  2. AWS Certified Machine Learning - Specialty

AWS Certified Machine Learning - Specialty

Amazon Web Services (AWS)

Certification

Become a contributor for free to openly demonstrate student outcomes, industry alignment & eligibility criteria.

The AWS Certified Machine Learning - Specialty (MLS-C01) exam is intended for individuals who perform an artificial intelligence and 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.

Cost

Exam Fee: $300 USDShow moreShow less

Format

Online

Eligibility Calculator

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Program Pathways

Credentials this program stacks toward

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Program Details

Detailed information about this program

The AWS Certified Machine Learning - Specialty (MLS-C01) exam is intended for individuals who perform an artificial intelligence and 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: - Select and justify the appropriate ML approach for a given business problem. - Identify appropriate AWS services to implement ML solutions. - Design and implement scalable, cost-optimized, reliable, and secure ML solutions. The target candidate should have 2 or more years of experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud. Recommended AWS knowledge The target candidate should have the following AWS knowledge: - Experience performing basic hyperparameter optimization - Experience with ML and deep learning frameworks Job tasks that are 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: - Extensive or complex algorithm development - Extensive hyperparameter optimization - Complex mathematical proofs and computations - Advanced networking and network design - Advanced database, security, and DevOps concepts - DevOps-related tasks for Amazon EMR Exam Content There are two types of questions on the exam: - 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 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. 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. 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.

Requirements

What you need to earn this credential

No requirements listed.

Financial Aid

Eligible funding programs

No funding information available.

Scholarships

No scholarships listed.

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Locations

Where this program is offered

No locations specified.

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Related Programs

Programs related to this one

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Skills & Competencies

Skills developed through this program

  • Design, build, deploy, optimize, train, tune, and maintain machine learning solutions using AWS Cloud for business problems
  • Select and justify the appropriate machine learning approach for a given business problem in AWS
  • Identify appropriate AWS services to implement machine learning solutions
  • Design and implement scalable, cost-optimized, reliable, and secure machine learning solutions
Career Pathways

Occupations this program prepares you for

  • Computer Systems Engineers/Architects15-1299.08
What You'll Learn

Key competencies developed through this program

Auto-populated·from NSX Competency Framework

Mastery: developing (Level 2)(based on Certification)

  • Client system requirements — analyze and translate into preliminary technical specifications with reduced oversight in a professional services environment.
  • Hardware and software component recommendations — evaluate suitability for defined purposes and present options to project stakeholders using structured comparison frameworks.
  • Secure system implementation guidelines — prepare and communicate tailored security configuration instructions to installation teams for mid-scale deployments.
  • Operating systems and network software — coordinate and execute installation or upgrade projects across multiple workstations or servers with minimal supervision.
  • System operation monitoring — apply monitoring tools to detect performance degradation or failure indicators and initiate standard corrective responses.
  • User need assessments — identify required system data, hardware, and software components by conducting structured needs-analysis sessions with end users.
  • Object-oriented or web platform development software — configure and adapt development environments to support application deployment on assigned systems.
  • Technical documentation — produce clear written procedures and configuration guides using document management software for internal or client audiences.
  • Routine hardware and software maintenance — plan and execute scheduled maintenance cycles including upgrades to minimize operational downtime.
  • Systems evaluation — apply analytical methods to assess existing infrastructure performance against defined benchmarks in familiar organizational contexts.

Some details on this page are auto-populated from public workforce data sources: O*NET (opens in new tab), BLS (opens in new tab), College Scorecard (opens in new tab), DOL Training Provider Results (opens in new tab), NSX (opens in new tab). Provided in partnership with LER.me Career Intelligence.

Student Outcomes

Performance metrics for this program

Auto-populated·from Scorecard + DOL
Completion Rate
100%
Placement Rate
Not reported