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Amazon MLS-C01 Korean Valid Braindumps - AWS Certified Machine Learning - Specialty (MLS-C01 Korean Version)

MLS-C01 Korean
  • Exam Code: MLS-C01-KR
  • Exam Name: AWS Certified Machine Learning - Specialty (MLS-C01 Korean Version)
  • Updated: May 11, 2025
  • Q & A: 324 Questions and Answers
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About Amazon MLS-C01 Korean Exam

How to book the AWS Certified Machine Learning - Specialty

These are following steps for registering the Amazon MLS exam:

  • Step 1 : Signup/Login to AWS account
  • Step 2 : Search for the exam
  • Step 3 : Select Date and Center of examination and confirm with payment

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Questions and answers from the AWS Certified Machine Learning Specialty include important topics from the AWS Certified Machine Learning Specialty Certification Program and provides easy-to-learn information for easy access.

The AWS Certified Machine Learning - Specialty (MLS-C01) examination is intended for individuals who perform a development or data science role. This exam validates an examinee's ability to build, train, tune, and deploy machine learning (ML) models using the AWS Cloud.

Candidate must have 1-2 years of hands-on experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud, along with:

  • The ability to follow deployment and operational best practices
  • The ability to follow model-training best practices
  • The ability to express the intuition behind basic ML algorithms
  • Experience performing basic hyperparameter optimization
  • Experience with ML and deep learning frameworks

Candidates for the AWS Certified Machine Learning Specialty should have a thorough knowledge and understanding of all the questions and answers of the AWS Certified Machine Learning Specialty in our practice exam and exam dumps.

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Amazon MLS-C01 Korean Exam Syllabus Topics:

TopicDetails
Topic 1
  • Machine Learning Implementation and Operations: Building ML solutions for performance, availability, scalability, resiliency, and fault tolerance is discussed in this topic. It also focuses on suitable ML services and features for a given problem. Lastly, the topic delves into applying basic AWS security practices to ML solutions and deploying and operationalizing ML solutions.
Topic 2
  • Modeling: The topic of modeling deals with framing business problems as ML problems, choosing the suitable model(s) for a given ML problem, training ML models. It also discusses hyperparameter optimization and evaluation of ML models.
Topic 3
  • Data Engineering: It discusses creating data repositories for ML, identifying and implementing a data ingestion solution. Lastly, the topic delves into identifying and implementing a data transformation solution.
Topic 4
  • Exploratory Data Analysis: This topic covers sanitizing and preparing data for modeling and performing feature engineering. Additionally, it discusses analyzing and visualizing data for ML.

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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