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Amazon AWS-Certified-Machine-Learning-Specialty

AWS-Certified-Machine-Learning-Specialty

Exam Code: AWS-Certified-Machine-Learning-Specialty

Exam Name: AWS Certified Machine Learning - Specialty

Updated: May 31, 2026

Q & A: 330 Questions and Answers

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About Amazon AWS-Certified-Machine-Learning-Specialty Exam

Training Options for MLS-C01 Exam

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:

  • Deep Learning on AWS

    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.

  • Process Model: CRISP-DM on the AWS Stack (by AWS Training)

    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.

  • The Machine Learning Pipeline on AWS (by AWS Training)

    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.

Preparation Process

Many useful resources are available for the Amazon MLS-C01 exam. Let’s take a closer look at them.

  • Discussion Forums

    Besides the official training options offered by the vendor, many websites provide the candidates with the topic details and useful information related to the exam as well as the certification. Some of these sites include Quora and ExamLabs.com.

  • AWS Machine Learning Documentation

    The official AWS Machine Learning Documentation is one of the best preparation guides for the Amazon AWS Certified Machine Learning – Specialty exam. It is a great material to learn all the topics included in the exam content. You can find documentation, such as splitting data, data transformation, as well as types of data.

  • AWS Training Courses

    The vendor offers the official Amazon AWS Machine Learning Certification Training, which you can access by visiting the Amazon website. It is good to know that these training courses are usually available as free.

Read the registration procedure of the AWS Certified Machine Learning Specialty Exam

In order to apply for the AWS Certified Machine Learning Specialty, You have to follow these steps

  • Step 1: Sign in to AWS Training
  • Step 2: Click Certification in the top navigation
  • Step 3: Click AWS Certification Account Button
  • Step 4: Followed by Schedule New Exam
  • Step 5: Search the AWS Certified Machine Learning Specialty exam
  • Step 6: Click either the Schedule at PSI or Schedule at Pearson VUE button
  • Step 7: Select Date, time and Schedule your test

Reference: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS%20Certified%20Machine%20Learning%20-%20Specialty_Exam%20Guide%20(1).pdf

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