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

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SASInstitute A00-255 Exam Syllabus Topics:

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

1. Assume that a company has an excellent customer segmentation in place and the segment scheme is a variable in the input data set. What is the best partition method that one should use?
Select one:
Response:

A) Random
B) Cluster
C) Systemic
D) Stratify

2. If we were to add a Transformation node, what would be the default transformation for interval inputs for the present scenario?
Response:

A) none of the above
B) Maximum Correlation
C) Maximum Normal
D) Optimal

3. 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) 0.15-0.499999
B) 0.85 or more
C) less than 0.149999
D) 0.50-0.849999

4. Consider a binary target variable. Assume Accuracy is the desired assessment measure. Accuracy is not an option in the Decision Tree node. Which assessment measure can you use as a proxy for accuracy?
Select one:
Response:

A) Mean Square Error
B) Average Squared Error
C) Total Profit
D) 1 - Misclassification Rate

5. The importance of an input variable in predicting a target in an MLP-based neural network can be figured out by which of the following?
Response:

A) none of the above
B) the highest absolute value of the parameter estimate between the input and any of the hidden neurons multiplied by the absolute value of the parameter estimate of the hidden neuron
C) the average of the absolute values of parameter estimates between the input and all of the hidden neurons
D) the highest absolute value of the parameter estimate between the input and any of the hidden neurons

Solutions: