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SASInstitute A00-255 (SAS Predictive Modeling Using SAS Enterprise Miner 14) Certification Exam covers essential topics such as: data preparation, variable selection, model development, model deployment, performance evaluation, and interpretation of results. It is suitable for professionals who work with large datasets, and who need to leverage advanced analytics techniques to identify insights and reveal patterns in data. A00-255 exam has been designed to validate candidates’ knowledge of the advanced features and functionality of SAS Enterprise Miner 14, providing them with the ability to use the tool in a professional setting.
NEW QUESTION # 21
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. 1.25-.49999
- B. 1.5-1.74999
- C. 0-1.24999
- D. 1.75 or more
Answer: B
NEW QUESTION # 22
Which of the following is not true about results produced by the Regression node?
Response:
- A. Type 3 Analysis of Effects provides you with information about the number of parameters that each input contributes to the model.
- B. Model Information provides you with information that includes the number of target categories and the number of model parameters.
- C. Variable Summary information identifies the roles of variables used by the Regression node.
- D. Fit Statistics can provide information that affects decision predictions, but does not affect estimate predictions.
Answer: D
NEW QUESTION # 23
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.
How many leaves are there in the decision tree?
Response:
- A. 1-10
- B. 16-20
- C. 11-15
- D. 21 or more
Answer: C
NEW QUESTION # 24
Which of the following sequential selection methods do you use so that SAS Enterprise Miner will look at all variables already included in the model and delete any variable that is not significant at the specified level?
Response:
- A. Forward
- B. None
- C. Stepwise
- D. Backward
Answer: B
NEW QUESTION # 25
An analyst is performing a market basket analysis (affinity analysis) on the purchase of Shaving Cream and Seltzer Water. The purchase data from a set of 250 customers is shown below:
What is the confidence of the rule "Shaving Cream implies Seltzer Water"? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:
- A. 40%
- B. 57%
- C. 67%
- D. 60%
Answer: B
NEW QUESTION # 26
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.
What percentage of all observations is being correctly predicted in the test data set by the decision tree?
Response:
- A. 85.2222%
- B. 16.8874%
- C. 83.1126%
- D. 84.5212%
Answer: C
NEW QUESTION # 27
Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:
- A. Ensemble
- B. Regression
- C. Decision Tree
- D. Neural Network
Answer: A
NEW QUESTION # 28
The selected model, based on the misclassification rate for the validation data, has how many input variables?
Response:
- A. 0
- B. 1
- C. 2
- D. 4 or more
Answer: B
NEW QUESTION # 29
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. Another benefit is ease in model interpretation.
- C. One benefit is improved model performance.
- D. Regression models are sensitive to extreme or outlying values in the input space.
Answer: B
NEW QUESTION # 30
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:
- A. Sample Method: Random and Criterion: Proportional
- B. Sample Method: Stratify and Criterion: Equal
- C. Sample Method: Random and Criterion: Equal
- D. Sample Method: Stratify and Criterion: Proportional
Answer: B
NEW 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:
- A. 1,600 or higher
- B. 300-999
- C. less than or equal to 299
- D. 1,000-1,599
Answer: A
NEW QUESTION # 32
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. 80% or higher
- D. less than 15%
Answer: D
NEW QUESTION # 33
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: A
NEW QUESTION # 34
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. ROC index, ASE, misclassification rate
- B. ASE, Lift for the top 10%, ROC index
- C. Misclassification rate, ROC index, ASE
- D. R-square, AIC, K-S statistic
Answer: A
NEW QUESTION # 35
Which statement describes the Decision Tree Split Search mechanism for categorical inputs?
Select one:
Response:
- A. All levels are weighted and the weights are used for testing.
- B. 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.
- C. The levels that have target rate of 0 or 100% are re-binned first, then weighted and the weights are used for testing.
- D. The average target value is calculated for each level, and then passed on for testing if it is the optimal split point.
Answer: D
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 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:
- A. 0.85 or more
- B. less than 0.149999
- C. 0.50-0.849999
- D. 0.15-0.499999
Answer: B
NEW QUESTION # 37
Impute the missing values for the variable TLSum using the Tree method. What is the mean of the new variable (with the imputed values)?
Response:
- A. less than 19,999.99
- B. 40,000 or higher
- C. 20,000-29,999.99
- D. 30,000-39,999.99
Answer: A
NEW QUESTION # 38
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. 1.2
- C. 0.8
- D. 0.05
Answer: D
NEW QUESTION # 39
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:
- A. 0.157016
- B. 0.162252
- C. 0.10
- D. 0.154788
Answer: A
NEW QUESTION # 40
The Chi Square statistic for measuring association between the variables BanruptcyInd and TARGET is which of the following?
Response:
- A. less than 1.00
- B. between 1.00 and 1.99
- C. 3.00 or higher
- D. between 2.00 and 2.99
Answer: B
NEW QUESTION # 41
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. 0-24.99
- C. 50-74.99
- D. 25-49.99
Answer: A
NEW QUESTION # 42
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SASInstitute A00-255 Exam is ideal for anyone who works with data and wants to demonstrate their expertise in predictive modeling with SAS Enterprise Miner 14. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification is particularly beneficial for professionals in business analytics, data analysis, market research, data science, and data management. Obtaining this certification not only validates the candidate's proficiency in SAS Enterprise Miner 14 but also demonstrates their commitment to ongoing learning and professional development.
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