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

A00-255

Exam Code: A00-255

Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14

Updated: Oct 14, 2026

Q & A: 87 Questions and Answers

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SASInstitute A00-255 Exam Syllabus Topics:

SectionObjectives
Topic 1: Business Understanding and Analytical Framework- Define business objectives and analytics goals
- Translate business problems into data mining tasks
Topic 2: Data Understanding and Preparation- Feature selection and transformation
- Handling missing values and outliers
- Data cleaning and preprocessing
- Data collection and data source identification
Topic 3: Exploratory Data Analysis- Visualization techniques for pattern discovery
- Descriptive statistics and data profiling
Topic 4: Model Evaluation and Validation- Model performance metrics
- Model comparison and selection
- Validation and cross-validation techniques
Topic 5: Model Development- Decision trees and ensemble methods
- Neural networks and advanced modeling in SAS Enterprise Miner
- Regression modeling techniques
Topic 6: Model Implementation and Deployment- Monitoring model performance in production
- Model scoring and deployment in SAS Enterprise Miner

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

Question #1
For the variable InqTimeLast, which term best describes the shape of its distribution?
Response:

A. bimodal
B. symmetric
C. right skewed
D. left skewed


Question #2
Refer to the following profit matrix and confusion matrix for a campaign soliciting product purchases. The predicted variable is a binary outcome.

Based on the above tables, what is the average profit? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

A. 690
B. 69
C. 86.25
D. 6.9


Question #3
Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

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


Question #4
Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:

A. Decision Tree
B. Neural Network
C. Ensemble
D. Regression


Question #5
How many unique indicator variables were created during imputation?
Response:

A. 11-20
B. 1-10
C. none
D. 21 or higher


Solutions:

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

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