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SAS Global Certification program


Predictive Modeler

SAS Certified Predictive Modeler using SAS Enterprise Miner 7 Credential

Designed for SAS Enterprise Miner users who perform predictive analytics

During this performance-based examination, candidates will use SAS Enterprise Miner to perform the examination tasks. It is essential that the candidate have a firm understanding and mastery of the functionalities for predictive modeling available in SAS Enterprise Miner 7.

Successful candidates should have the ability to
  • prepare data
  • build predictive models
  • assess models
  • score new data sets
  • implement models.
Candidates who earn this credential will have earned a passing score on the Predictive Modeling using SAS Enterprise Miner 7 exam *.

* This credential and exam were updated to 7.1 effective Jan 1, 2013. There are minimal differences between the 6.1 and 7.1 versions of the exam, and the recommended training course is appropriate for 6.1 or 7.1. Candidates who have taken training at the 6.1 level would not be disadvantaged in taking the 7.1 version exam. Candidates see the same case study/data as before and would need to take the same actions.

Required Exam

Candidates who earn this credential will have earned a passing score on the Predictive Modeling Using SAS Enterprise Miner 7 Exam.
  • 61 multiple-choice questions (must achieve score of 70% correct to pass)
  • 3 hours to complete exam
  • Candidates will use SAS Enterprise Miner to perform this exam
  • Exam offered exclusively at SAS training centers

Exam topics include:

Data Preparation
  • Starting a new Enterprise Miner project
  • Missing values
  • Initial data exploration including data visualization/measurement levels or scales/variable reduction
  • Transformation/recoding/binning
Predictive Models
  • Data splitting/balancing/overfitting/oversampling
  • Logistic/linear regression
  • Artificial neural networks (MLP)
  • Decision trees
  • Variable importance/odds ratio
  • Profit/loss/prior probabilities
Model Assessment
  • Comparison between models/lift chart/ROC/profit & loss
  • Assessment of a single model
Scoring and Implementation
  • Score a data set
  • Model implementation