A00-255 DUMP & FREE A00-255 EXAM

A00-255 Dump & Free A00-255 Exam

A00-255 Dump & Free A00-255 Exam

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q57-Q62):

NEW QUESTION # 57
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. 0-1.24999
  • B. 1.25-.49999
  • C. 1.5-1.74999
  • D. 1.75 or more

Answer: C


NEW QUESTION # 58
Refer to the exhibit:

What would be the decision threshold (probability cutoff) generated for this decision matrix. You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

  • A. 0.18
  • B. 0.21
  • C. 0.82
  • D. 0.27

Answer: A


NEW QUESTION # 59
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.
The number of parameters (weights) estimated by the Neural Network model is in which of the following ranges?
Response:

  • A. 16 or more
  • B. less than or equal to 5
  • C. 11-15
  • D. 6-10

Answer: A


NEW QUESTION # 60
If the bank wanted to select the best model based on the models' overall performances on the validation data as measured by the average squared error, then the best model is which of the following?
Response:

  • A. Decision Tree (3-way)
  • B. Decision Tree
  • C. Regression
  • D. Neural Network

Answer: C


NEW QUESTION # 61
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 dat a. 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 percentage of TARGET=1 as predicted by the best model on the scoring data is in which of the following ranges?
Response:

  • A. under 4.99%
  • B. 5%-5.99%
  • C. 7% or higher
  • D. 6%-6.99%

Answer: A


NEW QUESTION # 62
......

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