[Apr-2025] A00-255 Dumps are Available for Instant Access using DumpsMaterials [Q20-Q42]


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[Apr-2025] A00-255 Dumps are Available for Instant Access using DumpsMaterials

A00-255 Dumps 2025 – New SASInstitute A00-255 Exam Questions

SASInstitute A00-255 certification exam is all about SAS predictive modeling using SAS Enterprise Miner 14. The main aim of this certification is to test your knowledge and skills in predictive modeling techniques and how you can use SAS Enterprise Miner to solve business problems. The A00-255 certification exam is not only essential for SAS users but also for data scientists, statisticians, and business analysts who want to sharpen their skills in predictive modeling using SAS tools.

SAS Predictive Modeling Using SAS Enterprise Miner 14 is a certification exam offered by SAS Institute that validates the candidate’s knowledge and skills in using SAS Enterprise Miner for predictive modeling. A00-255 exam is designed to test the candidate’s ability to develop predictive models using various statistical and machine learning techniques, as well as their understanding of the data mining process.

 

QUESTION 20
Choose the correct statement that illustrates Decision Tree Split Search for continuous (interval) inputs:
Select one:
Response:

 
 
 
 

QUESTION 21
What percentage of observations in the test data has TARGET=1?
Response:

 
 
 
 

QUESTION 22
Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

 
 
 
 

QUESTION 23
What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:

 
 
 
 

QUESTION 24
Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:

1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Cubic Clustering Criterion statistic for this clustering?
Response:

 
 
 
 

QUESTION 25
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 decision tree model, what is the importance of the variable InqCnt06?
Response:

 
 
 
 

QUESTION 26
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:

 
 
 
 

QUESTION 27
For the variable InqTimeLast, which term best describes the shape of its distribution?
Response:

 
 
 
 

QUESTION 28
Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:

1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Cubic Clustering Criterion statistic for this clustering?
Response:

 
 
 
 

QUESTION 29
What is the number of missing values for the TLSum variable in the sample generated by SAS Enterprise Miner?
Response:

 
 
 
 

QUESTION 30
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:

 
 
 
 

QUESTION 31
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.
Now suppose that the bank expects to make a profit of $200 USD when TARGET=1, but it expects to lose $25 USD when TARGET=0. Incorporate the above scenario, change the assessment measure of the decision tree to average square error, and then run the Decision Tree node. What is the total profit for the test data set?
Response:

 
 
 
 

QUESTION 32
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:

 
 
 
 

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:

 
 
 
 

QUESTION 34
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.
Suppose that the data has been oversampled and the probability that TARGET=1 is 0.10 in the population. Incorporate the above scenario and run the Decision Tree node again.
What is the misclassification rate in the validation data set?
Response:

 
 
 
 

QUESTION 35
Refer to the exhibit:

The SAS data set credit_customers contains a numeric variable units_sold that holds only the values: 1, 2, 3, 4. Based on the settings provided in the Advanced Advisor Options, what will be the Role and Level of the units_sold variable when the credit_customers data set is created using Advanced Metadata Advisor in the Data Source Wizard?
Select one:
Response:

 
 
 
 

QUESTION 36
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:

 
 
 
 

QUESTION 37
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:

 
 
 
 

QUESTION 38
Refer to the following profit matrix and confusion matrix for a campaign soliciting product purchases. The predicted variable is a binary outcome.

Based on the above tables, what is the average profit? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

 
 
 
 

QUESTION 39
In segment 2, what percentage of GiftAvgCard36 values are between 6.6638 and 11.998?
Select one:
Response:

 
 
 
 

QUESTION 40
Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:

 
 
 
 

QUESTION 41
1. Define a new data source, PatternData, in SAS Enterprise Miner (SAS data set Patterndata.sas7bdat in the zip file distributed with this practice exam).
2. Set the role of all variables to Input, with the exception set the ID variable role to ID.
3. Set the measurement level for all variables to Interval, except:
– Set DemHomeOwner and StatusCatStarAll to Binary.
– Set DemCluster, DemGender, ID, and StatusCat96NK to Nominal.
4. Create a new diagram (name it Section6) within the project labeled Test.
5. Add the data source, PatternData, to this diagram. Make sure the variable roles and measurements are the same as in the table below. (Check the highlighted rows carefully and reset roles/levels as needed.)
6. Connect a Cluster node to the data source.
7. Modify the Cluster node to exclude nominal and binary input variables.
8. Run the Cluster node.

How many clusters are created by the Cluster node?
Response:

 
 
 
 

QUESTION 42
For the variable TLCnt24, apply a Max Normal transformation. What transformation was selected by SAS Enterprise Miner?
Response:

 
 
 
 

SASInstitute A00-255 Exam Practice Test Questions: https://www.dumpsmaterials.com/A00-255-real-torrent.html

         

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