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SASInstitute A00-255 exam consists of multiple-choice and short-answer questions that cover a wide range of topics related to predictive modeling using SAS Enterprise Miner. These topics include data preparation, variable selection, model building and assessment, model deployment, and model management. A00-255 Exam also covers advanced topics such as ensemble modeling, time series modeling, and text mining.

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q18-Q23):

NEW QUESTION # 18
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.
Which of the following variables was used in the decision tree model?
Response:

A. TLDel3060Cnt24
B. IMP_TLSatCnt
C. TLDel90Cnt24
D. InqFinanceCnt24.

Answer: A

NEW QUESTION # 19
Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:

A. decrease the performance of logistic regression
B. neither increase nor decrease the performance of logistic regression
C. create convergence problems in maximum likelihood estimation
D. increase the performance of logistic regression

Answer: D

NEW QUESTION # 20
1. Define a new data source, PatternData, in SAS Enterprise Miner (SAS data set Patterndata.sas7bdat in the zip file distributed with this practice exam).
2. Set the role of all variables to Input, with the exception set the ID variable role to ID.
3. Set the measurement level for all variables to Interval, except:
- Set DemHomeOwner and StatusCatStarAll to Binary.
- Set DemCluster, DemGender, ID, and StatusCat96NK to Nominal.
4. Create a new diagram (name it Section6) within the project labeled Test.
5. Add the data source, PatternData, to this diagram. Make sure the variable roles and measurements are the same as in the table below. (Check the highlighted rows carefully and reset roles/levels as needed.)
6. Connect a Cluster node to the data source.
7. Modify the Cluster node to exclude nominal and binary input variables.
8. Run the Cluster node.

How many clusters are created by the Cluster node?
Response:

A. 0
B. 1
C. 2
D. 3

Answer: B

NEW QUESTION # 21
You are building a model for a marketing campaign. Every responder to the campaign solicitation will generate $471 in gross revenue. The average cost per solicitation is $66. Incorporating the above information in a decision matrix, what would be the decision threshold (probability cutoff) generated in your model?
You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

A. 0.86
B. 0.16
C. 0.14
D. 0.20

Answer: C

NEW QUESTION # 22
Which statement describes the Decision Tree Split Search mechanism for categorical inputs?
Select one:
Response:

A. The average target value is calculated for each level, and then passed on for testing if it is the optimal split point.
B. The levels that have target rate of 0 or 100% are re-binned first, then weighted and the weights are used for testing.
C. All levels are weighted and the weights are used for testing.
D. A clustering mechanism eliminates observations in outlier clusters as potential split points as a first step. Then, for the remaining observations, the average target value is calculated for each level, and then passed on for testing if it is the optimal split point.

Answer: A

NEW QUESTION # 23
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