100% PASS QUIZ 2025 SASINSTITUTE A00-255: HIGH PASS-RATE LATEST SAS PREDICTIVE MODELING USING SAS ENTERPRISE MINER 14 DUMPS EBOOK

100% Pass Quiz 2025 SASInstitute A00-255: High Pass-Rate Latest SAS Predictive Modeling Using SAS Enterprise Miner 14 Dumps Ebook

100% Pass Quiz 2025 SASInstitute A00-255: High Pass-Rate Latest SAS Predictive Modeling Using SAS Enterprise Miner 14 Dumps Ebook

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The SAS Predictive Modeling Using SAS Enterprise Miner 14 certification exam is a great way for professionals to validate their skills and knowledge in this field. It is a globally recognized certification that demonstrates the candidate's expertise in SAS predictive modeling. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification exam can help professionals in getting better job opportunities and higher salaries.

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To sit for the SASInstitute A00-255 certification exam, candidates are required to have a solid understanding of statistics, data analysis, and predictive modeling concepts. They should be well-versed in SAS programming and have a good grasp of SAS Enterprise Miner interface. A00-255 exam consists of 50 multiple-choice questions and has a duration of 1.5 hours. To pass the exam, candidates need to score at least 68% and demonstrate their ability to apply the knowledge and skills acquired during their training to real-world scenarios. Achieving the SASInstitute A00-255 Certification is a valuable asset for data analysts, statisticians, and other professionals involved in predictive modeling, as it proves their mastery of one of the most popular data mining tools in the industry.

SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q31-Q36):

NEW QUESTION # 31
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.
What is the probability that TARGET=0 for ID=000355 in the training data?
Response:

  • A. 0.9341825902
  • B. 0.077935227
  • C. 0.0658174098
  • D. 0.9220647773

Answer: D


NEW QUESTION # 32
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: A


NEW QUESTION # 33
Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

  • A. The optimization for the model has not been completed.
  • B. All of the above
  • C. The misclassification error for the test data is 0.154255.
  • D. The model has too few input variables.

Answer: A


NEW QUESTION # 34
Refer to the graphs shown below. The graphs are from a study of response rate to a marketing campaign.

How much more likely are the top 20% of targeted respondents to purchase the product than a randomly selected sample?
Select one:
Response:

  • A. 60%
  • B. 25%
  • C. 30%
  • D. 140%

Answer: D


NEW QUESTION # 35
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

Answer: D


NEW QUESTION # 36
......

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