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A00-255 Test Lab Questions | A00-255 Online Lab Simulation

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SASInstitute A00-255 Exam consists of multiple-choice questions that cover various topics related to predictive modeling, including data preparation, variable selection, model assessment, and deployment. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification program is suitable for data analysts, statisticians, and data scientists who want to enhance their skills in data mining and predictive modeling.

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q32-Q37):

NEW QUESTION # 32
If the bank wanted to select the best model based on which model achieves the highest lift for the third decile in the validation data, then which of the following is the best model?
Response:

A. Regression
B. Neural Network
C. Decision Tree
D. Decision Tree (3-way)

Answer: C

NEW QUESTION # 33
Consider a binary target variable. Assume Accuracy is the desired assessment measure. Accuracy is not an option in the Decision Tree node. Which assessment measure can you use as a proxy for accuracy?
Select one:
Response:

A. Mean Square Error
B. Total Profit
C. 1 - Misclassification Rate
D. Average Squared Error

Answer: C

NEW QUESTION # 34
What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:

A. 0.27649
B. 0.24169
C. 0.10844
D. 0.24914

Answer: A

NEW QUESTION # 35
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. ASE, Lift for the top 10%, ROC index
B. ROC index, ASE, misclassification rate
C. R-square, AIC, K-S statistic
D. Misclassification rate, ROC index, ASE

Answer: B

NEW QUESTION # 36
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. right skewed
B. left skewed
C. bimodal
D. normal

Answer: B

NEW QUESTION # 37
......

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