2025 Realistic Verified A00-255 exam dumps Q&As - A00-255 Free Update
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SASInstitute A00-255 (SAS Predictive Modeling Using SAS Enterprise Miner 14) Certification Exam is a professional certification test that validates a candidate's knowledge and skills in predictive modeling using SAS Enterprise Miner 14. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification exam is designed for individuals who have a strong understanding of statistical concepts and are experienced in data analysis. A00-255 exam is intended to help professionals enhance their career opportunities and demonstrate their expertise in the field of predictive modeling.
SASInstitute A00-255 certification exam is ideal for professionals who want to enhance their career in the field of data science, machine learning, and predictive modeling. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification is suitable for data analysts, data scientists, statisticians, and business analysts who want to demonstrate their expertise in predictive modeling using SAS Enterprise Miner.
NEW QUESTION # 26
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. Gini coefficient
- B. KS statistic
- C. average squared error
- D. misclassification
Answer: A
NEW QUESTION # 27
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. 6-10
- B. less than or equal to 5
- C. 11-15
- D. 16 or more
Answer: D
NEW QUESTION # 28
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:
- A. Random
- B. Stratify
- C. Cluster
- D. Systemic
Answer: B
NEW QUESTION # 29
Which method of input selection for regression analysis evaluates the statistical significance of the total model to see if it improves on the baseline as the variables are added and once no further improvement is made then variable selection ends?
Select one:
Response:
- A. Forward
- B. Simple
- C. Stepwise
- D. Backward
Answer: A
NEW QUESTION # 30
Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:
- A. The model has too few input variables.
- B. All of the above
- C. The optimization for the model has not been completed.
- D. The misclassification error for the test data is 0.154255.
Answer: C
NEW QUESTION # 31
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:
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
NEW QUESTION # 32
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. Stepwise
- C. None
- D. Backward
Answer: C
NEW QUESTION # 33
The importance of an input variable in predicting a target in an MLP-based neural network can be figured out by which of the following?
Response:
- A. the highest absolute value of the parameter estimate between the input and any of the hidden neurons
- B. the average of the absolute values of parameter estimates between the input and all of the hidden neurons
- C. none of the above
- D. the highest absolute value of the parameter estimate between the input and any of the hidden neurons multiplied by the absolute value of the parameter estimate of the hidden neuron
Answer: C
NEW QUESTION # 34
Choose the correct statement that illustrates Decision Tree Split Search for continuous (interval) inputs:
Select one:
Response:
- A. The variable goes through a non-linear transformation, and the transformed variable is used for testing.
- B. The variable goes through a binning process, the bins are weighted based on the proportion of events in each bin, and then finally tested as an optimal split point.
- C. Each unique value has the potential of being the optimal split point.
- D. Each unique value has the potential of being the optimal split point, except for the extreme observation.
Answer: C
NEW QUESTION # 35
The number of neurons in this Neural Network model is which of the following:
Response:
- A. 4 or more
- B. 0
- C. 1
- D. 2
Answer: D
NEW QUESTION # 36
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 is the probability that TARGET=0 for ID=000355 in the training data?
Response:
- A. 0.9220647773
- B. 0.077935227
- C. 0.0658174098
- D. 0.9341825902
Answer: A
NEW 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:
- A. bimodal
- B. left skewed
- C. right skewed
- D. normal
Answer: B
NEW QUESTION # 38
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:
- A. TLDel3060Cnt24
- B. InqFinanceCnt24.
- C. IMP_TLSatCnt
- D. TLDel90Cnt24
Answer: A
NEW QUESTION # 39
What percentage of observations in the test data has TARGET=1?
Response:
- A. 16.8874
- B. 16.5924
- C. 83.3333
- D. 16.6627
Answer: A
NEW QUESTION # 40
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. less than or equal to 299
- B. 1,600 or higher
- C. 1,000-1,599
- D. 300-999
Answer: B
NEW QUESTION # 41
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. 0.27
- B. 0.82
- C. 0.21
- D. 0.18
Answer: D
NEW QUESTION # 42
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. 1.75 or more
- B. 0-1.24999
- C. 1.5-1.74999
- D. 1.25-.49999
Answer: C
NEW QUESTION # 43
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 Correlation
- D. Maximum Normal
Answer: A
NEW QUESTION # 44
What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:
- A. 0.24169
- B. 0.24914
- C. 0.27649
- D. 0.10844
Answer: C
NEW QUESTION # 45
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. 30%
- B. 140%
- C. 60%
- D. 25%
Answer: B
NEW QUESTION # 46
The selected model, based on the misclassification rate for the validation data, has how many input variables?
Response:
- A. 4 or more
- B. 0
- C. 1
- D. 2
Answer: D
NEW QUESTION # 47
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:
- A. 0.45 or higher
- B. less than 0.149999
- C. 0.30-0.449999
- D. 0.15-0.299999
Answer: C
NEW QUESTION # 48
For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:
- A. It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
- B. It looks for a set of colinear inputs that correlate with the target.
- C. It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.
- D. It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.
Answer: A
NEW QUESTION # 49
Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:
- A. neither increase nor decrease the performance of logistic regression
- B. decrease the performance of logistic regression
- C. increase the performance of logistic regression
- D. create convergence problems in maximum likelihood estimation
Answer: C
NEW QUESTION # 50
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