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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 : A00-255 Exam
About SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 : A00-255 Exam
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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Sources | 20–25% | - Create data sources from SAS tables - Explore and assess data sources - Modify and prepare source data for modeling |
| Topic 2: Predictive Model Assessment and Implementation | 25–30% | - Adjust for oversampling and sampling methods - Evaluate performance via profit/loss and comparison - Apply appropriate fit statistics - Score and deploy models |
| Topic 3: Building Predictive Models | 35–40% | - Build models using decision trees - Build models using regression techniques - Understand predictive modeling concepts - Build models using neural networks |
| Topic 4: Pattern Analysis | 10–15% | - Interpret pattern discovery results - Identify clusters and segments |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Question #1
What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:
- A. 0.10844
- B. 0.24914
- C. 0.24169
- D. 0.27649
Correct Answer: D 🗳️
Question #2
For an ordinal variable with L levels, the number of possible splits in a decision tree is which of the following?
Response:
- A. 2L-1
- B. (L-1)/1
- C. 2L
- D. 2L-1-1
Correct Answer: D 🗳️
Question #3
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. lift
- B. sensitivity
- C. specificity
- D. confidence intervals
Correct Answer: A 🗳️
Question #4
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. Backward
- B. Stepwise
- C. Simple
- D. Forward
Correct Answer: D 🗳️
Question #5
Which statement describes the Decision Tree Split Search mechanism for categorical inputs?
Select one:
Response:
- A. The levels that have target rate of 0 or 100% are re-binned first, then 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 average target value is calculated for each level, and then passed on for testing if it is the optimal split point.
- D. All levels are weighted and the weights are used for testing.
Correct Answer: C 🗳️
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