Machine Learning Specialist

Exam Content Guide

Below we provide a list of the objectives that will be tested on the exam.
For more specific details about each objective download the complete exam content guide.

Data Sources (30%)

  • Create a project in Model Studio
  • Explore the data
  • Modify data
  • Reduce the dimensionality of the data
  • Use the VARIABLE SELECTION node to identify important variables

Building Models (50%)

  • Describe key supervised machine learning terms and concepts
  • Build models with decision trees and ensemble of trees
  • Build models with neural networks
  • Build models with support vector machines
  • Use Model Interpretability tools to explain black box models
  • Incorporate externally written code

Model Assessment and Deployment (20%)

  • Explain the principles of Model Assessment
  • Assess and compare models in Model Studio
  • Deploy a model

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