AWS Certified Machine Learning - Specialty (MLS-C01 Korean Version): MLS-C01 Exam

"AWS Certified Machine Learning - Specialty (MLS-C01 Korean Version)", also known as MLS-C01 exam, is a Amazon Certification. With the complete collection of questions and answers, PrepPDF has assembled to take you through 330 Q&As to your MLS-C01 Exam preparation. In the MLS-C01 exam resources, you will cover every field and category in AWS Certified Machine Learning Certification helping to ready you for your successful Amazon Certification.

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  • Exam Code: MLS-C01
  • Exam Name: AWS Certified Machine Learning - Specialty (MLS-C01 Korean Version)
  • Certification Provider: Amazon
  • Corresponding Certification: AWS Certified Machine Learning
  • Updated: Jul 23, 2026
  • No. of Questions: 330 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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AWS MLS-C01 Exam Certification Details:

Exam NameAWS Certified Machine Learning - Specialty (Machine Learning Specialty)
Sample QuestionsAWS MLS-C01 Sample Questions
Passing Score750 / 1000
Schedule ExamPEARSON VUE
Exam CodeMLS-C01
Duration180 minutes
Number of Questions65
Exam Price$300 USD
Recommended Training / BooksPractical Data Science with Amazon SageMaker
The Machine Learning Pipeline on AWS
Deep Learning on AWS

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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What is the duration of the AWS Certified Machine Learning - Specialty

  • Number of Questions: 54
  • Language : English, Japanese, Korean, and Simplified Chinese
  • Format: Multiple choices, multiple answers
  • Length of Examination: 130 minutes
  • Passing Score: 720

The benefit in Obtaining the AWS Certified Machine Learning - Specialty Certification

  • Amazon MLS certified candidates will be confident and stand different from others as their skills are more trained than non-certified professionals
  • Amazon MLS Certifications will provide opportunities to get a job easily in which they are interested in instead of wasting years and ending without getting any experience
  • AWS Certified Machine Learning - Specialty will provide proven knowledge to use the tools to complete the task efficiently and cost effectively than the other non-certified professionals lack in doing so
  • Amazon MLS Certification provides practical experience to candidates from all the aspects to be a proficient worker in the organization

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Introduction to AWS Certified Machine Learning Specialty Exam

The AWS Certified Machine Learning Exam - Special Exam (MLS-C01) is designed for people who have a development or data science function. This exam validates a candidate's ability to create, train, adapt and implement machine learning (ML) models using the AWS cloud. Validation of a candidate's ability to design, implement, implement and maintain ML solutions for certain business problems. It will validate the candidate's ability to: Select and justify the appropriate LD approach for a given business problem. Identify the appropriate AWS services to implement ML solutions. Design and implement scalable, economic, reliable and safe ML solutions.

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Amazon MLS-C01 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Engineering20%- Store data for ML workloads
  • 1. Select appropriate storage services
    • 2. Implement data lakes and data warehouses
      • 3. Manage data lifecycle and access
        - Ingest data from various sources
        • 1. Ensure data quality and consistency during ingestion
          • 2. Implement data ingestion mechanisms
            • 3. Determine data ingestion approach
              - Transform and prepare data
              • 1. Perform feature engineering
                • 2. Clean and preprocess data
                  • 3. Scale and normalize data
                    Topic 2: Machine Learning Implementation and Operations20%- Deploy ML models
                    • 1. Implement model serving endpoints
                      • 2. Automate deployment pipelines
                        • 3. Choose deployment options (real-time, batch, edge)
                          - Ensure security, compliance, and governance
                          • 1. Implement access control and encryption
                            • 2. Establish operational best practices
                              • 3. Follow compliance requirements
                                - Monitor and maintain models
                                • 1. Track model performance and drift
                                  • 2. Monitor data quality and integrity
                                    • 3. Update and retrain models as needed
                                      Topic 3: Exploratory Data Analysis24%- Validate data quality and integrity
                                      • 1. Assess data completeness and accuracy
                                        • 2. Verify data distribution and relationships
                                          • 3. Identify and resolve data biases
                                            - Interpret analysis results
                                            • 1. Derive insights from data
                                              • 2. Translate findings into ML requirements
                                                - Analyze and visualize data
                                                • 1. Apply statistical methods
                                                  • 2. Create visualizations to identify patterns
                                                    • 3. Detect anomalies and outliers
                                                      Topic 4: Modeling36%- Frame ML problems
                                                      • 1. Define success metrics and objectives
                                                        • 2. Classify problem type (classification, regression, clustering, etc.)
                                                          - Evaluate and optimize models
                                                          • 1. Test model performance
                                                            • 2. Address overfitting/underfitting
                                                              • 3. Optimize for cost, latency, and accuracy
                                                                - Train and tune models
                                                                • 1. Manage training jobs and resources
                                                                  • 2. Configure training environments
                                                                    • 3. Perform hyperparameter optimization
                                                                      - Select appropriate algorithms and models
                                                                      • 1. Compare custom vs built-in algorithms
                                                                        • 2. Select models based on performance and constraints
                                                                          • 3. Choose deep learning vs traditional ML approaches

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