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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building Predictive Models | 35–40% | - Understand predictive modeling concepts - Build models using neural networks - Build models using regression techniques - Build models using decision trees |
| Data Sources | 20–25% | - Explore and assess data sources - Modify and prepare source data for modeling - Create data sources from SAS tables |
| Pattern Analysis | 10–15% | - Interpret pattern discovery results - Identify clusters and segments |
| Predictive Model Assessment and Implementation | 25–30% | - Score and deploy models - Adjust for oversampling and sampling methods - Evaluate performance via profit/loss and comparison - Apply appropriate fit statistics |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Question 1
Which of the following is not true about results produced by the Regression node?
Response:
A. Variable Summary information identifies the roles of variables used by the Regression node.
B. Type 3 Analysis of Effects provides you with information about the number of parameters that each input contributes to the model.
C. Model Information provides you with information that includes the number of target categories and the number of model parameters.
D. Fit Statistics can provide information that affects decision predictions, but does not affect estimate predictions.
Question 2
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. 21 or more
B. 1-10
C. 11-15
D. 16-20
Question 3
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. 67%
B. 60%
C. 57%
D. 40%
Question 4
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. 6%-6.99%
B. under 4.99%
C. 7% or higher
D. 5%-5.99%
Question 5
The number of neurons in this Neural Network model is which of the following:
Response:
A. 2
B. 4 or more
C. 3
D. 1
Solutions:
| Question 1 Answer: D | Question 2 Answer: C | Question 3 Answer: C | Question 4 Answer: B | Question 5 Answer: D |
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