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Last Updated: Oct 11, 2026

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SASInstitute A00-255 Exam Syllabus Topics:

SectionWeightObjectives
Data Sources20-25%- Prepare Source Data
  • 1. Modify source data
  • 2. Prepare data for predictive modeling
- Create and Explore Data Sources
  • 1. Explore and assess data sources
  • 2. Create data sources from SAS tables in Enterprise Miner
Pattern Analysis10-15%- Segmentation and Association Analysis
  • 1. Perform sequence analysis and market basket analysis
  • 2. Perform association analysis
  • 3. Identify clusters using Cluster and Segment Profile nodes
Building Predictive Models35-40%- Model Development
  • 1. Build regression models
  • 2. Build decision tree models
  • 3. Build neural network models
  • 4. Describe predictive modeling concepts and terminology
Predictive Model Assessment and Implementation25-30%- Model Evaluation
  • 1. Select appropriate fit statistics
  • 2. Assess model performance using profit and loss information
  • 3. Compare models using Model Comparison node
  • 4. Use decision processing for oversampling adjustment
- Model Deployment
  • 1. Score data sets within Enterprise Miner

SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:

Question #1

Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

  • A. The model has too few input variables.
  • B. All of the above
  • C. The misclassification error for the test data is 0.154255.
  • D. The optimization for the model has not been completed.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #2

The number of hidden layers in this Neural Network model is which of the following?
Response:

  • A. 3
  • B. 4 or more
  • C. 1
  • D. 2
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #3

A useful concept in logistic regression is the doubling amount. How would you calculate doubling amount for an input variable that has a parameter estimate of b1?
Response:

  • A. 2/log(b1)
  • B. 0.69/b1
  • C. 2*log(b1)
  • D. 2*b1
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #4

Which of the following solves problems for you when you impute missing values?
Response:

  • A. When you impute a synthetic value, it replaces missing values with 1 or 0.
  • B. When you impute a synthetic value, each missing value becomes an input to the model.
  • C. When you impute a synthetic value, it eliminates the incomplete case problem.
  • D. When you impute a synthetic value, predictive information is retained.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #5

You are building a model to identify fraud. Your model will produce predictions that can be interpreted as the probability of fraud. You will pass on the top 100 scoring cases to management for investigation. Assume that you have sufficient data to hold out a validation and test data set for model evaluation.
Which selection would represent a reasonable ordering of fit statistics (best to worst) for this situation?
Select one:
Response:

  • A. Misclassification rate, ROC index, ASE
  • B. R-square, AIC, K-S statistic
  • C. ASE, Lift for the top 10%, ROC index
  • D. ROC index, ASE, misclassification rate
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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