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Get Latest Valid A00-255 Exam Forum and High Hit Rate Valid Braindumps A00-255 Sheet

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SASInstitute A00-255 exam is suitable for individuals who are proficient in SAS programming and have experience in data preparation, data mining, and data analysis. A00-255 exam objectives include understanding data exploration, data preparation, variable selection, modeling, and model assessment. A00-255 exam also covers advanced modeling techniques, such as decision trees, neural networks, and ensemble models.

SASInstitute A00-255 Exam is designed to test an individual's knowledge and skills in predictive modeling using SAS Enterprise Miner 14. A00-255 exam is intended for those who have experience in data analysis and want to specialize in predictive modeling. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification is recognized globally and is an excellent way for individuals to validate their skills in this field.

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q59-Q64):

NEW QUESTION # 59
Refer to the exhibit:

The SAS data set retail contains information on the count of retail store sales based on the following item types: bargain, essential, gourmet, and health. Based on the results from the Cluster Profile node, which statement is true?
Select one:
Response:

A. The overall distribution of essential item sales is approximately normal and Segment 1 contains stores selling fewer than average essential items.
B. The overall distribution of essential item sales is right skewed and Segment 4 contains stores selling higher than average essential items.
C. The overall distribution of bargain item sales is left-skewed and Segment 4 contains stores selling fewer than average bargain items.
D. The overall distribution of bargain item sales is approximately normal and Segment 1 contains stores selling fewer than average bargain items.

Answer: D

NEW QUESTION # 60
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 distribution of the predicted probabilities of TARGET=0 in the scoring data is approximately which of the following?
Response:

A. bimodal
B. right skewed
C. normal
D. left skewed

Answer: D

NEW QUESTION # 61
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. InqFinanceCnt24.
D. TLDel90Cnt24

Answer: A

NEW QUESTION # 62
If we were to add a Transformation node, what would be the default transformation for interval inputs for the present scenario?
Response:

A. Optimal
B. Maximum Normal
C. Maximum Correlation
D. none of the above

Answer: D

NEW QUESTION # 63
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.
In the training data set, consider only those observations for which the actual value of the target variable equals 1, TARGET=1. What percentage of these observations is being correctly predicted by the decision tree?
Response:

A. 0
B. 14.6216
C. 66.6667
D. 80.3571

Answer: A

NEW QUESTION # 64
......

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