AWS Machine Learning Specialty Certification Exam Syllabus

MLS-C01 Dumps Questions, MLS-C01 PDF, Machine Learning Specialty Exam Questions PDF, AWS MLS-C01 Dumps Free, Machine Learning Specialty Official Cert Guide PDFThe AWS MLS-C01 exam preparation guide is designed to provide candidates with necessary information about the Machine Learning Specialty exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the AWS Certified Machine Learning - Specialty exam.

It is recommended for all the candidates to refer the MLS-C01 objectives and sample questions provided in this preparation guide. The AWS Machine Learning Specialty certification is mainly targeted to the candidates who want to build their career in Specialty domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual AWS Certified Machine Learning - Specialty exam.

AWS MLS-C01 Exam Summary:

Exam Name
AWS Certified Machine Learning - Specialty (Machine Learning Specialty)
Exam Code MLS-C01
Exam Price $300 USD
Duration 180 minutes
Number of Questions 65
Passing Score 750 / 1000
Recommended Training / Books Practical Data Science with Amazon SageMaker
The Machine Learning Pipeline on AWS
Deep Learning on AWS
Schedule Exam PEARSON VUE
Sample Questions AWS MLS-C01 Sample Questions
Recommended Practice AWS Certified Machine Learning - Specialty Practice Test

AWS Machine Learning Specialty Syllabus:

Section Objectives Weight
Data Engineering - Create data repositories for machine learning.
- Identify and implement a data-ingestion solution.
- Identify and implement a data-transformation solution.
Exploratory Data Analysis -Sanitize and prepare data for modeling.
- Perform feature engineering.
- Analyze and visualize data for machine learning.
Modeling - Frame business problems as machine learning problems.
- Select the appropriate model(s) for a given machine learning problem.
- Train machine learning models.
- Perform hyperparameter optimization.
- Evaluate machine learning models.
Machine Learning Implementation and Operations - Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
- Recommend and implement the appropriate machine learning services and features for a given problem.
- Apply basic AWS security practices to machine learning solutions.
- Deploy and operationalize machine learning solutions.
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