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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Model Implementation and Deployment | - Model scoring and deployment in SAS Enterprise Miner - Monitoring model performance in production |
| Exploratory Data Analysis | - Visualization techniques for pattern discovery - Descriptive statistics and data profiling |
| Business Understanding and Analytical Framework | - Translate business problems into data mining tasks - Define business objectives and analytics goals |
| Model Development | - Neural networks and advanced modeling in SAS Enterprise Miner - Regression modeling techniques - Decision trees and ensemble methods |
| Data Understanding and Preparation | - Data cleaning and preprocessing - Data collection and data source identification - Feature selection and transformation - Handling missing values and outliers |
| Model Evaluation and Validation | - Model performance metrics - Validation and cross-validation techniques - Model comparison and selection |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
1. Impute the missing values for the variable TLSum using the Tree method. What is the mean of the new variable (with the imputed values)?
Response:
A) 30,000-39,999.99
B) less than 19,999.99
C) 40,000 or higher
D) 20,000-29,999.99
2. Refer to the following profit matrix and confusion matrix for a campaign soliciting product purchases. The predicted variable is a binary outcome.
Based on the above tables, what is the average profit? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:
A) 690
B) 6.9
C) 69
D) 86.25
3. A useful concept in logistic regression is the doubling amount. How would you calculate doubling amount for an input variable that has a parameter estimate of b1?
Response:
A) 2*log(b1)
B) 2/log(b1)
C) 0.69/b1
D) 2*b1
4. For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:
A) It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.
B) It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.
C) It looks for a set of colinear inputs that correlate with the target.
D) It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
5. 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) 0.9341825902
B) 0.9220647773
C) 0.0658174098
D) 0.077935227
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: B |
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