Exam Code: AWS-Certified-Machine-Learning-Specialty
Exam Name: AWS Certified Machine Learning - Specialty
Certification Provider: Amazon
Corresponding Certification: AWS Certified Machine Learning
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Understanding functional and technical aspects of AWS Certified Machine Learning - Specialty Exploratory Data Analysis

The following will be discussed in AMAZON MLS-C01 exam dumps:

  • Sanitize and prepare data for modeling
  • Analyze and visualize data for machine learning
  • Perform feature engineering

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 AWS-Certified-Machine-Learning-Specialty Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Exploratory Data Analysis24%- Sanitize and prepare data for modeling
  • 1. Validate dataset quality
  • 2. Detect and handle missing or anomalous data
- Perform exploratory analysis
  • 1. Visualize datasets and extract insights
  • 2. Analyze data distributions and correlations
Topic 2: Modeling36%- Evaluate model performance
  • 1. Interpret model metrics
  • 2. Address overfitting and underfitting
- Frame business problems as machine learning problems
  • 1. Select appropriate machine learning approaches
  • 2. Define evaluation metrics
- Train machine learning models
  • 1. Optimize training processes
  • 2. Choose suitable algorithms
  • 3. Tune hyperparameters
Topic 3: Data Engineering20%- Create data repositories for machine learning
  • 1. Select and implement data ingestion solutions
  • 2. Identify and store datasets for machine learning workloads
- Identify and implement data transformation solutions
  • 1. Clean and prepare datasets
  • 2. Perform feature engineering and data preprocessing
Topic 4: Machine Learning Implementation and Operations20%- Monitor and maintain machine learning solutions
  • 1. Manage retraining and versioning
  • 2. Track model performance in production
- Implement security and operational best practices
  • 1. Optimize scalability, reliability, and cost
  • 2. Apply IAM and data protection controls
- Deploy machine learning solutions
  • 1. Implement batch and real-time inference
  • 2. Select deployment architectures

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