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The AWS Machine Learning Specialty certification is designed for developers and data scientists who want to enhance their skills in using machine learning via the AWS platform.
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Career Opportunities
Machine Learning is no doubt one of the hottest topics within the Information Technology sector. Therefore, the Amazon AWS Certified Machine Learning – Specialty certification is simply the key to become a highly regarded certified professional in the field. Those professionals who obtain this certificate can boost their career to a higher level and get a decent salary. They can opt for different job roles, such as a Solutions Architect, a Technical Curriculum Developer, an Electrical Safety Program Manager, a Systems Development Engineer, a Software Development Manager, a Global Ergonomics Engineer, and many more. The average salary can range from $30,000 to $160,000 per year.
Prerequisites
The potential candidates should have 12-24 months of experience in architecting, developing, or running machine learning or deep learning workloads particularly on AWS Cloud. They must also possess the ability to effectively express the intuition behind basic machine learning algorithms. It is also advisable to have some experience with machine learning and deep learning frameworks, as well as be able to follow operational & deployment best practices.
AWS MLS-C01 Exam Certification Details:
| Exam Price | $300 USD |
| Schedule Exam | PEARSON VUE |
| Recommended Training / Books | Practical Data Science with Amazon SageMaker The Machine Learning Pipeline on AWS Deep Learning on AWS |
| Duration | 180 minutes |
| Exam Name | AWS Certified Machine Learning - Specialty (Machine Learning Specialty) |
| Number of Questions | 65 |
| Passing Score | 750 / 1000 |
| Exam Code | MLS-C01 |
| Sample Questions | AWS MLS-C01 Sample Questions |
Amazon MLS-C01日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning Operations | 15–20% | - Model deployment and serving
|
| Topic 2: Data Engineering | 20–25% | - Data ingestion
|
| Topic 3: Exploratory Data Analysis | 22–28% | - Data visualization and descriptive statistics
|
| Topic 4: Modeling | 32–38% | - Specialized models
|
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