[UPDATED] SASInstitute A00-255 Certification Exam Questions [Q25-Q49]

[UPDATED] SASInstitute A00-255 Certification Exam Questions

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SASInstitute A00-255 certification exam is a popular choice among professionals who want to establish themselves as experts in predictive modeling. A00-255 exam is designed to test the candidates’ knowledge and skills in using SAS Enterprise Miner 14 to build predictive models. A00-255 exam covers a wide range of topics, including data preparation, variable selection, model building, deployment, and validation.

The A00-255 certification exam consists of 70 multiple-choice and short-answer questions that need to be completed in 2 hours and 15 minutes. A00-255 exam is administered by Pearson VUE, a leading computer-based testing company that provides testing services for various industries. A00-255 exam fee is $180 USD and can be scheduled at any Pearson VUE testing center worldwide.

 

Q25. Which of the following is not true about results produced by the Regression node?
Response:

 
 
 
 

Q26. Perform these tasks in SAS Enterprise Miner:
– Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
– Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
Consider the variable TLCnt03 in the selected model. Based on the model results, changing this variable by 1 unit will result in which of the following?
Response:

 
 
 
 

Q27. Which statement describes the Decision Tree Split Search mechanism for categorical inputs?
Select one:
Response:

 
 
 
 

Q28. What is the purpose of the Kass (Bonferroni) adjustment in the decision tree split-search algorithm?
Select one:
Response:

 
 
 
 

Q29. A useful concept in logistic regression is the doubling amount. How would you calculate doubling amount for an input variable that has a parameter estimate of b1?
Response:

 
 
 
 

Q30. Which method of input selection for regression analysis evaluates the statistical significance of all included inputs after each input is added?
Select one:
Response:

 
 
 
 

Q31. What is the kurtosis value for the variable TLDel60Cnt24?
Response:

 
 
 
 

Q32. 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:

 
 
 
 

Q33. For the variable TLCnt24, apply a Max Normal transformation. What transformation was selected by SAS Enterprise Miner?
Response:

 
 
 
 

Q34. How many hidden layers are generally needed in an MLP-based neural network to capture a discontinuous relationship between inputs and target?
Response:

 
 
 
 

Q35. Perform these tasks in SAS Enterprise Miner:
– Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
– Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
Which of the following variable(s) is (are) statistically significant at the 5% level in the selected model?
Response:

 
 
 
 

Q36. Assume in a data mining project that the task is to predict rankings of a target variable as accurately as possible. Which of the following should be used to judge prediction models?
Response:

 
 
 
 

Q37. 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 data. 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 median of the predicted probabilities of TARGET=1 in the scoring data is in which of the following ranges?
Response:

 
 
 
 

Q38. 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:

 
 
 
 

Q39. You are building a model for a marketing campaign. Every responder to the campaign solicitation will generate $471 in gross revenue. The average cost per solicitation is $66. Incorporating the above information in a decision matrix, what would be the decision threshold (probability cutoff) generated in your model?
You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

 
 
 
 

Q40. Assume a variable is coded as follows: 1=unmarried, 2=married, 3=divorced, and 4=widowed. Then which of the following measurement levels should be selected in SAS Enterprise Miner for this variable?
Response:

 
 
 
 

Q41. Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:

 
 
 
 

Q42. 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.
In the validation data, the lift corresponding to the fourth decile is in which of the following ranges?
Response:

 
 
 
 

Q43. A separate sample has been taken such that the target distribution in the separate sample is different from the target distribution in the original sample.
What should be adjusted?
Select one:
Response:

 
 
 
 

Q44. What is the average squared error in the training data?
Response:

 
 
 
 

Q45. If we were to add a Transformation node, what would be the default transformation for interval inputs for the present scenario?
Response:

 
 
 
 

Q46. The Chi Square statistic for measuring association between the variables BanruptcyInd and TARGET is which of the following?
Response:

 
 
 
 

Q47. Assume the Target has an event proportion of 2% in the original data. Which of the following property values should be used in the Sample node of SAS Enterprise Miner to create a sample from that data with a balanced 50/50 split for Target?
Select one:
Response:

 
 
 
 

Q48. What percentage of observations in the test data has TARGET=1?
Response:

 
 
 
 

Q49. A multilayer perceptron neural network is using three interval inputs to model one interval target (outcome). The neural network has ten hidden units and one hidden layer. How many weights, including biases are being estimated?
You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

 
 
 
 

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SASInstitute A00-231 exam is intended for individuals who have a solid foundation in SAS programming and are seeking to demonstrate their proficiency to potential employers or clients. It is recommended that candidates have at least six months of experience working with SAS 9.4 Base Programming, as well as a strong understanding of key programming concepts such as data manipulation, syntax, and logic. Upon successful completion of the exam, candidates will earn the SAS Certified Base Programmer for SAS 9 credential, which is recognized as a valuable industry certification.

 

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SASInstitute A00-231 Certification Exam is a performance-based exam that assesses candidates’ proficiency in SAS 9.4 Base Programming. A00-231 exam covers a wide range of topics related to SAS programming and is suitable for individuals who are interested in pursuing a career in data analysis, business intelligence, or software development. Passing the exam demonstrates to potential employers or clients that an individual has the necessary skills and knowledge to work with SAS software effectively.

 

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