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Exam Number : MLS-C01
Exam Name : AWS Certified Machine Learning Specialty (MLS-C01)
Vendor Name : Amazon
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MLS-C01 test Format | MLS-C01 Course Contents | MLS-C01 Course Outline | MLS-C01 test Syllabus | MLS-C01 test Objectives


Format : Multiple choice, multiple answer
Type : Specialty
Delivery Method : Testing center or online proctored exam
Time : 170 minutes to complete the exam

Introduction
The AWS Certified Machine Learning - Specialty (MLS-C01) examination is intended for individuals who perform a development or data science role. This test validates an examinees ability to build, train, tune, and deploy machine learning (ML) models using the AWS Cloud.
It validates an examinees ability to design, implement, deploy, and maintain ML solutions for given business problems. It will validate the candidates ability to:
 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

Expectations;
- 1-2 years of experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud
- The ability to express the intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- The ability to follow model-training best practices
- The ability to follow deployment and operational best practices

Exam Content
There are two types of questions on the examination:
 Multiple choice: Has one correct response and three incorrect responses (distractors).
 Multiple response: Has two or more correct responses out of five or more options.
Select one or more responses that best complete the statement or answer the question. Distractors, or incorrect answers, are response options that an examinee with incomplete knowledge or skill would likely choose. However, they are generally plausible responses that fit in the content area defined by the test objective.
Unanswered questions are scored as incorrect; there is no penalty for guessing.

Unscored Content
Your examination may include unscored items that are placed on the test to gather statistical information. These items are not identified on the form and do not affect your score.

Exam Results
The AWS Certified Machine Learning - Specialty (MLS-C01) examination is a pass or fail exam. The examination is scored against a minimum standard established by AWS professionals who are guided by certification industry best practices and guidelines.
Your results for the examination are reported as a score from 100–1,000, with a minimum passing score of 750. Your score shows how you performed on the examination as a whole and whether or not you passed. Scaled scoring models are used to equate scores across multiple test forms that may have slightly different difficulty levels.

Domain 1: Data Engineering 20%
Domain 2: Exploratory Data Analysis 24%
Domain 3: Modeling 36%
Domain 4: Machine Learning Implementation and Operations 20%
TOTAL 100%

Domain 1: Data Engineering
1.1 Create data repositories for machine learning.
1.2 Identify and implement a data-ingestion solution.
1.3 Identify and implement a data-transformation solution.

Domain 2: Exploratory Data Analysis
2.1 Sanitize and prepare data for modeling.
2.2 Perform feature engineering.
2.3 Analyze and visualize data for machine learning.

Domain 3: Modeling
3.1 Frame business problems as machine learning problems.
3.2 Select the appropriate model(s) for a given machine learning problem.
3.3 Train machine learning models.
3.4 Perform hyperparameter optimization.
3.5 Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations
4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
4.2 Recommend and implement the appropriate machine learning services and features for a given problem.
4.3 Apply basic AWS security practices to machine learning solutions.
4.4 Deploy and operationalize machine learning solutions.



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