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The SASInstitute A00-255 exam comprises of 50 multiple choice questions and lasts for 110 minutes. A00-255 exam questions are designed to test the candidate's understanding of practical predictive modeling concepts and techniques as well as their ability to apply them using SAS Enterprise Miner. Candidates must demonstrate proficiency in a range of areas, including data preparation and exploration, modeling, model assessment and deployment, and data mining techniques. A passing score of 68% or higher is required to obtain the certification.

The A00-255 Exam consists of 50 multiple-choice and short-answer questions that cover a range of topics related to predictive modeling using SAS Enterprise Miner 14. These topics include data preparation, variable selection, model training and validation, model deployment, and performance evaluation.

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

NEW QUESTION # 59
If we were to add a Transformation node, what would be the default transformation for interval inputs for the present scenario?
Response:

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

Answer: A

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 percentage of TARGET=1 as predicted by the best model on the scoring data is in which of the following ranges?
Response:

A. 7% or higher
B. 5%-5.99%
C. 6%-6.99%
D. under 4.99%

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.
The number of parameters (weights) estimated by the Neural Network model is in which of the following ranges?
Response:

A. less than or equal to 5
B. 11-15
C. 16 or more
D. 6-10

Answer: C

NEW QUESTION # 62
What is the average squared error in the training data?
Response:

A. 0.131208
B. 0.133665
C. 0.131583
D. 0.131709

Answer: C

NEW QUESTION # 63
If you only consider observations for which TARGET=0, what percentage of such observations has BanruptcyInd=1?
Response:

A. 80% or higher
B. between 50%-79.99%
C. less than 15%
D. between 15%-49.99%

Answer: C

NEW QUESTION # 64
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