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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.
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SASInstitute A00-255 certification is a valuable credential for professionals who want to advance their careers in data science, business analytics, and related fields. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification demonstrates the candidates' expertise in using SAS Enterprise Miner to perform predictive modeling tasks, which is a critical skill in today's data-driven business environment. Holding the A00-255 Certification can also lead to better job opportunities and higher salaries.
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q69-Q74):
NEW QUESTION # 69
Which of the following solves problems for you when you impute missing values?
Response:
- A. When you impute a synthetic value, it replaces missing values with 1 or 0.
- B. When you impute a synthetic value, predictive information is retained.
- C. When you impute a synthetic value, each missing value becomes an input to the model.
- D. When you impute a synthetic value, it eliminates the incomplete case problem.
Answer: D
NEW QUESTION # 70
In SAS Enterprise Miner's Decision Tree node, which of the following types of target variable can be used?
Response:
- A. binary
- B. all of the above
- C. nominal with any number of categories
- D. interval
Answer: B
NEW QUESTION # 71
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: B
NEW QUESTION # 72
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. under 4.99%
- B. 6%-6.99%
- C. 7% or higher
- D. 5%-5.99%
Answer: A
NEW QUESTION # 73
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. Optimal
- C. none of the above
- D. Maximum Correlation
Answer: C
NEW QUESTION # 74
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