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Amazon MLS-C01日本語 Exam : AWS Certified Machine Learning - Specialty (MLS-C01日本語版)

MLS-C01日本語 actual test
  • Exam Code: AWS-Certified-Machine-Learning-Specialty-JPN
  • Exam Name: AWS Certified Machine Learning - Specialty (MLS-C01日本語版)
  • Updated: Jul 21, 2026
  • Q & A: 330 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $69.99  

About Amazon MLS-C01日本語 Exam

Understanding functional and technical aspects of AWS Certified Machine Learning Specialty Exam Modeling

The following will be dicussed here:

  • Perform hyperparameter optimization
  • Train machine learning models
  • Frame business problems as machine learning problems
  • Select the appropriate model(s) for a given machine learning problem
  • Evaluate machine learning models

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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Recommended Experience

Before registering for the AWS Certified Machine Learning – Specialty exam, the applicant should ensure meeting some prerequisites as stated by the vendor. First, working experience of 1-2 years in running ML workloads as well as their development and architecting on AWS cloud is a must for the candidate. Moreover, it is recommended to have practical skills in executing the hyperparameter optimization, deep learning and ML frameworks, and operational and model-training best practices for AWS Machine learning.

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References:

AWS Certified Machine Learning Specialty Exam Reference

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Amazon MLS-C01日本語 Exam Syllabus Topics:

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

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