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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Exploratory Data Analysis | - Visualization techniques for pattern discovery - Descriptive statistics and data profiling |
| Topic 2: Model Evaluation and Validation | - Model performance metrics - Model comparison and selection - Validation and cross-validation techniques |
| Topic 3: Data Understanding and Preparation | - Data collection and data source identification - Handling missing values and outliers - Data cleaning and preprocessing - Feature selection and transformation |
| Topic 4: Business Understanding and Analytical Framework | - Translate business problems into data mining tasks - Define business objectives and analytics goals |
| Topic 5: Model Implementation and Deployment | - Monitoring model performance in production - Model scoring and deployment in SAS Enterprise Miner |
| Topic 6: Model Development | - Neural networks and advanced modeling in SAS Enterprise Miner - Decision trees and ensemble methods - Regression modeling techniques |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Question 1
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. 5%-5.99%
D. 7% or higher
Question 2
If the bank wanted to select the best model based on the models' overall performances on the validation data as measured by the average squared error, then the best model is which of the following?
Response:
A. Decision Tree
B. Neural Network
C. Regression
D. Decision Tree (3-way)
Question 3
Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:
1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Cubic Clustering Criterion statistic for this clustering?
Response:
A. 5862.76
B. 67409.93
C. 14.69
D. 5.00
Question 4
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. 0.9341825902
D. 0.0658174098
Question 5
You are building a model for a marketing campaign. Every responder to the campaign solicitation will generate $471 in gross revenue. The average cost per solicitation is $66. Incorporating the above information in a decision matrix, what would be the decision threshold (probability cutoff) generated in your model?
You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:
A. 0.86
B. 0.14
C. 0.20
D. 0.16
Solutions:
| Question 1 Answer: A | Question 2 Answer: C | Question 3 Answer: C | Question 4 Answer: A | Question 5 Answer: B |






