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| Section | Weight | Objectives |
|---|---|---|
| Data Sources | 20–25% | - Create data sources from SAS tables - Explore and assess data sources - Modify and prepare source data for modeling |
| Building Predictive Models | 35–40% | - Build models using decision trees - Build models using regression techniques - Build models using neural networks - Understand predictive modeling concepts |
| Pattern Analysis | 10–15% | - Interpret pattern discovery results - Identify clusters and segments |
| Predictive Model Assessment and Implementation | 25–30% | - Apply appropriate fit statistics - Adjust for oversampling and sampling methods - Evaluate performance via profit/loss and comparison - Score and deploy models |
1. Consider a binary target variable. Assume Accuracy is the desired assessment measure. Accuracy is not an option in the Decision Tree node. Which assessment measure can you use as a proxy for accuracy?
Select one:
Response:
A) Total Profit
B) 1 - Misclassification Rate
C) Average Squared Error
D) Mean Square Error
2. Refer to the exhibit:
When the Explore button is selected in the graphic, what information will be displayed?
Select one:
Response:
A) Sample Properties, Sample Statistics for all variables, and sample data for all variables
B) Sample Properties, Sample Statistics, sample data with all variables and values, and a histogram of the data for the selected variable
C) Sample Properties, Sample Statistics, sample data for the selected variable, and a histogram of the data for the selected variable
D) Sample Properties, Sample Statistics, sample data for the selected variable, and a pie chart of the selected variable
3. 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) 300-999
C) 1,000-1,599
D) 1,600 or higher
4. The number of hidden layers in this Neural Network model is which of the following?
Response:
A) 3
B) 2
C) 1
D) 4 or more
5. 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) 60%
B) 40%
C) 67%
D) 57%
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: D |
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