SAS Predictive Modeling Using SAS Enterprise Miner 14: A00-255 Exam

"SAS Predictive Modeling Using SAS Enterprise Miner 14", also known as A00-255 exam, is a SASInstitute Certification. With the complete collection of questions and answers, PrepPDF has assembled to take you through 87 Q&As to your A00-255 Exam preparation. In the A00-255 exam resources, you will cover every field and category in SAS Institute SAS Certification helping to ready you for your successful SASInstitute Certification.

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  • Exam Code: A00-255
  • Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14
  • Certification Provider: SASInstitute
  • Corresponding Certification: SAS Institute SAS
  • Updated: Aug 20, 2026
  • No. of Questions: 87 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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SASInstitute A00-255 Exam Syllabus Topics:

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

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

1. Refer to the graphs shown below. The graphs are from a study of response rate to a marketing campaign.

How much more likely are the top 20% of targeted respondents to purchase the product than a randomly selected sample?
Select one:
Response:

A) 140%
B) 30%
C) 60%
D) 25%


2. Impute the missing values for the variable TLSum using the Tree method. What is the mean of the new variable (with the imputed values)?
Response:

A) 20,000-29,999.99
B) less than 19,999.99
C) 40,000 or higher
D) 30,000-39,999.99


3. Which method of input selection for regression analysis evaluates the statistical significance of all included inputs after each input is added?
Select one:
Response:

A) Backward
B) Simple
C) Stepwise
D) Forward


4. What is the number of missing values for the TLSum variable in the sample generated by SAS Enterprise Miner?
Response:

A) 1-19
B) 0
C) 20-39
D) 40 or more


5. Assume you have two equally appealing logistic regression models. Then, if you have to select only one out of these two models, you should select the one that has which of the following?
Response:

A) higher value of AIC (Akaike,s information criterion)
B) smaller value of SBC (Schwarz,s Bayesian criterion)
C) all of the above
D) smaller value of gamma


Solutions:

Question # 1
Answer: A
Question # 2
Answer: B
Question # 3
Answer: C
Question # 4
Answer: D
Question # 5
Answer: B

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