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

MLS-C01 Korean Exam Simulator
  • Exam Code: AWS-Certified-Machine-Learning-Specialty-KR
  • Exam Name: AWS Certified Machine Learning - Specialty (MLS-C01 Korean Version)
  • Updated: Jul 21, 2026
  • Q & A: 330 Questions and Answers
  • Amazon MLS-C01 Korean Q&A - in .pdf

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AWS MLS-C01 Exam Certification Details:

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

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.

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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Amazon MLS-C01 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Exploratory Data Analysis22–28%- Data quality assessment
  • 1. Evaluate data completeness, accuracy, and consistency
  • 2. Detect and resolve data bias and leakage
- Data visualization and descriptive statistics
  • 1. Analyze data distributions and relationships
  • 2. Identify patterns, trends, and anomalies
- Feature engineering
  • 1. Create, transform, and select features
  • 2. Reduce dimensionality and handle feature correlation
Topic 2: Modeling32–38%- Model evaluation and optimization
  • 1. Tune hyperparameters and improve generalization
  • 2. Validate performance using proper metrics
- Specialized models
  • 1. Ensemble methods and transfer learning
  • 2. Deep learning, time-series, NLP, and computer vision
- Model selection and training
  • 1. Choose appropriate algorithms and approaches
  • 2. Train models using Amazon SageMaker and other tools
Topic 3: Data Engineering20–25%- Data preparation and transformation
  • 1. Handle missing values, outliers, and imbalanced datasets
  • 2. Clean, preprocess, and normalize data
- Data storage and management
  • 1. Select appropriate storage solutions
  • 2. Ensure data security, privacy, and compliance
- Data ingestion
  • 1. Implement data pipelines using AWS services
  • 2. Collect and ingest data from various sources
Topic 4: Machine Learning Operations15–20%- Monitoring and maintenance
  • 1. Update, retrain, and version models
  • 2. Track model performance and data drift
- Security, governance, and compliance
  • 1. Implement audit trails and regulatory requirements
  • 2. Secure ML pipelines and access controls
- Model deployment and serving
  • 1. Deploy models to production environments
  • 2. Implement real-time and batch inference

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