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SASInstitute A00-255 Practical Information, Question A00-255 Explanations

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SASInstitute A00-255 Exam requires a thorough understanding of statistical concepts, data analysis techniques, and machine learning algorithms. A00-255 exam covers topics such as linear regression, logistic regression, decision trees, clustering, and neural networks, among others. It also requires a good understanding of SAS programming language and SAS Enterprise Miner 14.

>> SASInstitute A00-255 Practical Information <<

SASInstitute A00-255 SAS Predictive Modeling Using SAS Enterprise Miner 14 Questions - With 25% Discount Offer [2025]

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q53-Q58):

NEW QUESTION # 53
For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:

A. It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.
B. It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
C. It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.
D. It looks for a set of colinear inputs that correlate with the target.

Answer: B

NEW QUESTION # 54
Assume that a company has an excellent customer segmentation in place and the segment scheme is a variable in the input data set. What is the best partition method that one should use?
Select one:
Response:

A. Systemic
B. Random
C. Cluster
D. Stratify

Answer: D

NEW QUESTION # 55
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. TLDel90Cnt24
C. IMP_TLSatCnt
D. InqFinanceCnt24.

Answer: A

NEW QUESTION # 56
You are building a model to identify fraud. Your model will produce predictions that can be interpreted as the probability of fraud. You will pass on the top 100 scoring cases to management for investigation. Assume that you have sufficient data to hold out a validation and test data set for model evaluation.
Which selection would represent a reasonable ordering of fit statistics (best to worst) for this situation?
Select one:
Response:

A. ROC index, ASE, misclassification rate
B. R-square, AIC, K-S statistic
C. ASE, Lift for the top 10%, ROC index
D. Misclassification rate, ROC index, ASE

Answer: A

NEW QUESTION # 57
What is the purpose of the Kass (Bonferroni) adjustment in the decision tree split-search algorithm?
Select one:
Response:

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

Answer: C

NEW QUESTION # 58
......

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