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SASInstitute A00-255 Exam Syllabus Topics:

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

1. 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) 6.9
B) 86.25
C) 69
D) 690

2. Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
In the validation data, the lift corresponding to the fourth decile is in which of the following ranges?
Response:

A) 1.25-.49999
B) 0-1.24999
C) 1.75 or more
D) 1.5-1.74999

3. For the variable InqCnt06, replace all values over 10.1 with the value 10. How many values are replaced?
Response:

A) 100-149
B) 200 or higher
C) 0-99
D) 150-199

4. Assume in a data mining project that the task is to predict rankings of a target variable as accurately as possible. Which of the following should be used to judge prediction models?
Response:

A) misclassification
B) average squared error
C) KS statistic
D) Gini coefficient

5. Perform these tasks in SAS Enterprise Miner:
* Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT data. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:

The median of the predicted probabilities of TARGET=1 in the scoring data is in which of the following ranges?
Response:

A) 0.50-0.849999
B) 0.15-0.499999
C) less than 0.149999
D) 0.85 or more

Solutions: