A00-255 PRINTABLE PDF, EXAM A00-255 REFERENCE

A00-255 Printable PDF, Exam A00-255 Reference

A00-255 Printable PDF, Exam A00-255 Reference

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Tags: A00-255 Printable PDF, Exam A00-255 Reference, Latest A00-255 Learning Materials, Exam A00-255 Tests, Valid A00-255 Vce

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SASInstitute A00-255 Exam is an essential certification for professionals who are interested in advancing their careers in the field of predictive modeling. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification provides individuals with the credentials required to demonstrate their expertise in working with the SAS Enterprise Miner software program. The SASInstitute A00-255 Exam is an industry-recognized certification that is highly respected in the field of predictive modeling and data analysis.

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Exam A00-255 Reference - Latest A00-255 Learning Materials

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q50-Q55):

NEW QUESTION # 50
What percentage of observations in the test data has TARGET=1?
Response:

  • A. 16.6627
  • B. 16.5924
  • C. 16.8874
  • D. 83.3333

Answer: C


NEW QUESTION # 51
The number of neurons in this Neural Network model is which of the following:
Response:

  • A. 0
  • B. 4 or more
  • C. 1
  • D. 2

Answer: D


NEW QUESTION # 52
For the variable InqCnt06, replace all values over 10.1 with the value 10. How many values are replaced?
Response:

  • A. 0-99
  • B. 200 or higher
  • C. 150-199
  • D. 100-149

Answer: D


NEW QUESTION # 53
Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:

1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Cubic Clustering Criterion statistic for this clustering?
Response:

  • A. 5.00
  • B. 67409.93
  • C. 5862.76
  • D. 14.69

Answer: D


NEW QUESTION # 54
Which of the following is not a good reason to"regularize" input distributions using a simple transformation?
Response:

  • A. One benefit is improved model performance.
  • B. Regression models are sensitive to extreme or outlying values in the input space.
  • C. Another benefit is ease in model interpretation.
  • D. When you perform regression, inputs with highly skewed or highly kurtotic distributions can be selected over inputs that would yield better overall predictions.

Answer: C


NEW QUESTION # 55
......

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