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Updated: Jul 23, 2026

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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.

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

Prerequisites

The potential candidates should have 12-24 months of experience in architecting, developing, or running machine learning or deep learning workloads particularly on AWS Cloud. They must also possess the ability to effectively express the intuition behind basic machine learning algorithms. It is also advisable to have some experience with machine learning and deep learning frameworks, as well as be able to follow operational & deployment best practices.

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

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.

Amazon MLS-C01日本語 Exam Overview:

Certification Vendor:Amazon AWS
Exam Name:AWS Certified Machine Learning - Specialty
Exam Number:MLS-C01
Related Certifications:AWS Certified Data Analytics - Specialty
AWS Certified Solutions Architect - Professional
AWS Certified DevOps Engineer - Professional
Certificate Validity Period:3 years
Exam Duration:180 minutes
Passing Score:750 (scaled score 100–1000)
Real Exam Qty:65 (50 scored, 15 unscored)
Exam Format:Multiple choice, Multiple response
Available Languages:English, Japanese, Korean, Simplified Chinese
Exam Price:300 USD
Recommended Training:AWS Certified Machine Learning - Specialty Exam Guide
AWS Training and Certification
Exam Registration:Pearson VUE Registration
AWS Certification Registration
Sample Questions:Amazon MLS-C01日本語 Sample Questions
Exam Way:Online proctored or at Pearson VUE test centers
Pre Condition:Recommended: 2+ years of experience building, architecting, and running ML/deep learning workloads on AWS; AWS Associate or Professional certifications recommended but not required
Official Syllabus URL:https://docs.aws.amazon.com/aws-certification/latest/machine-learning-specialty-01/machine-learning-specialty-01.html

Amazon MLS-C01日本語 Exam Syllabus Topics:

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

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