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

1. Refer to the exhibit:

The SAS data set retail contains information on the count of retail store sales based on the following item types: bargain, essential, gourmet, and health. Based on the results from the Cluster Profile node, which statement is true?
Select one:
Response:

A) The overall distribution of essential item sales is approximately normal and Segment 1 contains stores selling fewer than average essential items.
B) The overall distribution of bargain item sales is approximately normal and Segment 1 contains stores selling fewer than average bargain items.
C) The overall distribution of bargain item sales is left-skewed and Segment 4 contains stores selling fewer than average bargain items.
D) The overall distribution of essential item sales is right skewed and Segment 4 contains stores selling higher than average essential items.

2. In SAS Enterprise Miner's Decision Tree node, which of the following types of target variable can be used?
Response:

A) all of the above
B) interval
C) binary
D) nominal with any number of categories

3. What is the purpose of the Kass (Bonferroni) adjustment in the decision tree split-search algorithm?
Select one:
Response:

A) To give categorical inputs a greater chance to be used the split.
B) To ensure that the choice of split is not influenced by input measurement scale.
C) To ensure a non-negative logworth value.
D) To reduce the number of surrogate splitting rules.

4. Refer to the exhibit:

The SAS data set credit_customers contains a numeric variable units_sold that holds only the values: 1, 2, 3, 4. Based on the settings provided in the Advanced Advisor Options, what will be the Role and Level of the units_sold variable when the credit_customers data set is created using Advanced Metadata Advisor in the Data Source Wizard?
Select one:
Response:

A) Role: InputLevel: Interval
B) Role: InputLevel: Nominal
C) Role: RejectedLevel: Nominal
D) Role: IntervalLevel: Input

5. Perform these tasks in SAS Enterprise Miner:
Add a Decision Tree node, as shown below. (Make sure you use only default options in the Decision Tree node.)

Run the Decision Tree node.
Now suppose that the bank expects to make a profit of $200 USD when TARGET=1, but it expects to lose $25 USD when TARGET=0. Incorporate the above scenario, change the assessment measure of the decision tree to average square error, and then run the Decision Tree node. What is the total profit for the test data set?
Response:

A) 300-999
B) 1,600 or higher
C) 1,000-1,599
D) less than or equal to 299

Solutions: