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SASInstitute A00-255 certification exam is an excellent opportunity for individuals seeking to enhance their skills in predictive modeling using SAS Enterprise Miner 14. It validates one's knowledge and expertise in data mining and predictive modeling and provides a competitive edge in the job market. With the right preparation and practice, passing A00-255 exam can significantly boost one's career prospects in the field of data science.
NEW QUESTION # 18
Sometimes in predictive modeling we build models using a sample that has a primary outcome proportion different from true population proportion. This is usually done when the ratio of primary to secondary outcomes in a binary target variable in the population is close to which of the following?
Response:
- A. 0
- B. 0.8
- C. 1.2
- D. 0.05
Answer: D
NEW QUESTION # 19
Refer to the graphs shown below. The graphs are from a study of response rate to a marketing campaign.
How much more likely are the top 20% of targeted respondents to purchase the product than a randomly selected sample?
Select one:
Response:
- A. 140%
- B. 30%
- C. 60%
- D. 25%
Answer: A
NEW QUESTION # 20
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:
- A. 0
- B. 1
- C. 2
- D. 3
Answer: D
NEW QUESTION # 21
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.
For the validation data, in what range does cumulative percent captured response at the 60th percentile lie?
Response:
- A. 75 or more
- B. 50-74.99
- C. 0-24.99
- D. 25-49.99
Answer: A
NEW QUESTION # 22
-> 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).
- Allow for 1 substitute rule in case the variable for the primary splitting rule is missing.
- Disable pruning for the decision tree.
-> 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.
-> Run the process flow.
What is the number of input variables being used by the Neural Network Model?
Enter your numeric answer in the space below:
Response:
- A. 0
- B. 1
- C. 2
- D. 3
Answer: B
NEW QUESTION # 23
The number of hidden layers in this Neural Network model is which of the following?
Response:
- A. 0
- B. 4 or more
- C. 1
- D. 2
Answer: C
NEW QUESTION # 24
Which method of input selection for regression analysis evaluates the statistical significance of all included inputs after each input is added?
Select one:
Response:
- A. Backward
- B. Simple
- C. Forward
- D. Stepwise
Answer: D
NEW QUESTION # 25
Refer to the exhibit:
When the Explore button is selected in the graphic, what information will be displayed?
Select one:
Response:
- A. Sample Properties, Sample Statistics, sample data with all variables and values, and a histogram of the data for the selected variable
- B. Sample Properties, Sample Statistics for all variables, and sample data for all variables
- C. Sample Properties, Sample Statistics, sample data for the selected variable, and a pie chart of the selected variable
- D. Sample Properties, Sample Statistics, sample data for the selected variable, and a histogram of the data for the selected variable
Answer: D
NEW QUESTION # 26
Which statement describes the Decision Tree Split Search mechanism for categorical inputs?
Select one:
Response:
- A. The average target value is calculated for each level, and then passed on for testing if it is the optimal split point.
- B. The levels that have target rate of 0 or 100% are re-binned first, then weighted and the weights are used for testing.
- C. All levels are weighted and the weights are used for testing.
- D. A clustering mechanism eliminates observations in outlier clusters as potential split points as a first step. Then, for the remaining observations, the average target value is calculated for each level, and then passed on for testing if it is the optimal split point.
Answer: A
NEW QUESTION # 27
For the variable TLCnt24, apply a Max Normal transformation. What transformation was selected by SAS Enterprise Miner?
Response:
- A. Square Root
- B. Exponential
- C. Square
- D. Log
Answer: A
NEW QUESTION # 28
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 bargain item sales is approximately normal and Segment 1 contains stores selling fewer than average bargain 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 essential item sales is right skewed and Segment 4 contains stores selling higher than average essential items.
Answer: B
NEW QUESTION # 29
What is the purpose of the Kass (Bonferroni) adjustment in the decision tree split-search algorithm?
Select one:
Response:
- A. To ensure that the choice of split is not influenced by input measurement scale.
- B. To reduce the number of surrogate splitting rules.
- C. To give categorical inputs a greater chance to be used the split.
- D. To ensure a non-negative logworth value.
Answer: A
NEW QUESTION # 30
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. R-square, AIC, K-S statistic
- B. ROC index, ASE, misclassification rate
- C. ASE, Lift for the top 10%, ROC index
- D. Misclassification rate, ROC index, ASE
Answer: B
NEW QUESTION # 31
If we were to add a Transformation node, what would be the default transformation for interval inputs for the present scenario?
Response:
- A. Maximum Normal
- B. Maximum Correlation
- C. none of the above
- D. Optimal
Answer: C
NEW QUESTION # 32
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:
- A. 1.5-1.74999
- B. 1.25-.49999
- C. 1.75 or more
- D. 0-1.24999
Answer: A
NEW QUESTION # 33
If you only consider observations for which TARGET=0, what percentage of such observations has BanruptcyInd=1?
Response:
- A. between 50%-79.99%
- B. between 15%-49.99%
- C. less than 15%
- D. 80% or higher
Answer: C
NEW QUESTION # 34
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. 5%-5.99%
- B. 6%-6.99%
- C. 7% or higher
- D. under 4.99%
Answer: D
NEW QUESTION # 35
Assume you have two equally appealing logistic regression models. Then, if you have to select only one out of these two models, you should select the one that has which of the following?
Response:
- A. all of the above
- B. higher value of AIC (Akaike,s information criterion)
- C. smaller value of SBC (Schwarz,s Bayesian criterion)
- D. smaller value of gamma
Answer: C
NEW QUESTION # 36
Suppose your input variables have missing values. Before running a decision tree with these input variables, you should do which of the following?
Response:
- A. impute only interval variables using the Tree method but do not impute the class variables
- B. not impute any missing values because trees can handle them.
- C. impute only class variables using the Tree method but do not impute the interval variables
- D. impute all missing values using the Tree method
Answer: B
NEW QUESTION # 37
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:
- A. sensitivity
- B. confidence intervals
- C. specificity
- D. lift
Answer: D
NEW QUESTION # 38
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:
- A. misclassification
- B. KS statistic
- C. Gini coefficient
- D. average squared error
Answer: C
NEW QUESTION # 39
What is the number of missing values for the TLSum variable in the sample generated by SAS Enterprise Miner?
Response:
- A. 20-39
- B. 1-19
- C. 40 or more
- D. 0
Answer: C
NEW QUESTION # 40
Refer to the exhibit:
What would be the decision threshold (probability cutoff) generated for this decision matrix. You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:
- A. 0.82
- B. 0.18
- C. 0.27
- D. 0.21
Answer: B
NEW QUESTION # 41
Which of the following is not a good reason to"regularize" input distributions using a simple transformation?
Response:
- A. When you perform regression, inputs with highly skewed or highly kurtotic distributions can be selected over inputs that would yield better overall predictions.
- B. One benefit is improved model performance.
- C. Regression models are sensitive to extreme or outlying values in the input space.
- D. Another benefit is ease in model interpretation.
Answer: D
NEW QUESTION # 42
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To appear for the SASInstitute A00-255 Certification Exam, candidates must have a strong understanding of SAS programming basics, statistical modeling concepts, and data mining techniques. A00-255 exam focuses on various topics, such as data exploration, data transformation, variable selection, model comparison, and model deployment. Candidates must also have experience working with SAS Enterprise Miner software to analyze and model data effectively. Passing the SASInstitute A00-255 Certification Exam demonstrates that a candidate has the necessary skills and knowledge to use SAS Enterprise Miner effectively for predictive modeling and data analysis.
SASInstitute A00-255 certification exam is designed for professionals who want to validate their skills and knowledge in predictive modeling using SAS Enterprise Miner 14. A00-255 exam tests the candidate's ability to use SAS Enterprise Miner to develop predictive models, assess model performance, and deploy models. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification is globally recognized and highly valued in the industry, making it an excellent choice for professionals who want to advance their careers in the field of predictive modeling.
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