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Updated: Jul 23, 2026
No. of Questions: 330 Questions & Answers with Testing Engine
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If you don't have much hands-on experience in machine learning, it is recommended to enroll in the course and gain it before going for the AWS Certified Machine Learning – Specialty exam. There are five options offered by AWS itself, some of them are as follows:
This course also lasts for only one day but still has comprehensive content. It emphasizes the AWS DL (Deep Learning) solutions as well as the use of MXNet and Amazon SageMaker. Also, the candidates are going to understand how to deploy DL models by utilizing AWS services and build intelligent systems. This training can be taken in either live-classroom form or live-virtual.
In contrast to the previous option, this training is just a one-day course. The candidates can follow it in different languages like English, French, Simplified Chinese, Indonesian, Japanese, and Korean. It focuses on the CRISP-DM model in relation to data science, including all its six phases and framework and methodology. The course also shows applicants how to use CRISP-DM to resolve various problems within their daily work. The good thing about this training is that it's free.
During these in-classroom or virtual sessions, the candidates will come across exceptional knowledge about how to use the machine learning pipeline to solve real business problems. It is the best project-based studying environment for individuals that are passionate about working with ML models using Amazon SageMaker. At the end of the course, students will be able to solve any issues related to fraud detection, flight delays, recommendation engines, etc. It leads the applicants to the path of overcoming any challenges effectively and get knowledge of machine learning thoroughly to take the test. You will find this 4-days course easy if you have prior experience in the field as well as knowledge of Python and Statistics.
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.
In order to apply for the AWS Certified Machine Learning Specialty, You have to follow these steps
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.
| Certification Vendor: | Amazon AWS |
| Exam Name: | AWS Certified Machine Learning - Specialty |
| Exam Number: | MLS-C01 |
| Related Certifications: | AWS Certified Data Analytics - Specialty AWS Certified Solutions Architect - Professional AWS Certified DevOps Engineer - Professional |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 180 minutes |
| Passing Score: | 750 (scaled score 100–1000) |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Exam Format: | Multiple choice, Multiple response |
| Available Languages: | English, Japanese, Korean, Simplified Chinese |
| Exam Price: | 300 USD |
| Recommended Training: | AWS Certified Machine Learning - Specialty Exam Guide AWS Training and Certification |
| Exam Registration: | Pearson VUE Registration AWS Certification Registration |
| Sample Questions: | Amazon MLS-C01日本語 Sample Questions |
| Exam Way: | Online proctored or at Pearson VUE test centers |
| Pre Condition: | Recommended: 2+ years of experience building, architecting, and running ML/deep learning workloads on AWS; AWS Associate or Professional certifications recommended but not required |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/machine-learning-specialty-01/machine-learning-specialty-01.html |
| Section | Weight | Objectives |
|---|---|---|
| Modeling | 36% | - Select appropriate algorithms and models
|
| Machine Learning Implementation and Operations | 20% | - Monitor and maintain models
|
| Exploratory Data Analysis | 24% | - Analyze and visualize data
|
| Data Engineering | 20% | - Store data for ML workloads
|
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