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Amazon - MLS-C01 Certification Exam Details, Questions and Answers

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MLS-C01: AWS Certified Machine Learning - Specialty

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Updated on

25 January 2024
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Model Development Machine Learning Implementation and Operations AWS Machine Learning Services Data Engineering Exploratory Data Analysis

AWS Certified Machine Learning - Specialty (MLS-C01)

Understanding the Importance of this Certification

The AWS Certified Machine Learning - Specialty certification is designed for individuals who perform a development role and have one or more years of hands-on experience developing and maintaining an AWS-based application. This certification validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

Technical Details of the Certification Exam

The MLS-C01 exam is a multiple-choice, multiple-answer exam. The exam includes questions on AWS ML services and the basic principles of machine learning. It is recommended that individuals have hands-on experience using AWS services before taking the exam.

Measured Skills in the Exam

  • Ability to select and justify the appropriate ML approach for a given business problem
  • Ability to identify appropriate AWS services to implement ML solutions
  • Capability in designing and implementing scalable, cost-optimized, reliable, and secure ML solutions

Preparation Guidance for the Exam

Before taking the exam, you should have a clear understanding of the AWS platform, AWS ML services, and the basic principles of ML. AWS offers resources such as AWS Whitepapers, AWS FAQs, AWS Documentation, and AWS Training and Certification resources to help prepare for the exam.

Exam Topics

  • Model Development (20% - 30%)

    • Model selection
    • Model evaluation
    • Model optimization
    • Hyperparameter tuning
  • Machine Learning Implementation and Operations (15% - 25%)

    • Model deployment
    • Model monitoring
    • Model retraining
    • Model versioning
    • Model explainability
  • AWS Machine Learning Services (10% - 20%)

    • Amazon SageMaker
    • Amazon Comprehend
    • Amazon Rekognition
    • Amazon Forecast
    • Amazon Personalize
    • AWS DeepLens
  • Data Engineering (15% - 25%)

    • Data ingestion
    • Data transformation
    • Data storage
    • Data quality
    • Data governance
  • Exploratory Data Analysis (10% - 20%)

    • Data visualization
    • Statistical analysis
    • Feature engineering
    • Data preprocessing