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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Business Understanding and Analytical Framework | - Translate business problems into data mining tasks - Define business objectives and analytics goals |
| Topic 2: Model Evaluation and Validation | - Model comparison and selection - Model performance metrics - Validation and cross-validation techniques |
| Topic 3: Model Development | - Regression modeling techniques - Neural networks and advanced modeling in SAS Enterprise Miner - Decision trees and ensemble methods |
| Topic 4: Exploratory Data Analysis | - Visualization techniques for pattern discovery - Descriptive statistics and data profiling |
| Topic 5: Data Understanding and Preparation | - Handling missing values and outliers - Data cleaning and preprocessing - Data collection and data source identification - Feature selection and transformation |
| Topic 6: Model Implementation and Deployment | - Model scoring and deployment in SAS Enterprise Miner - Monitoring model performance in production |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Question 1
A multilayer perceptron neural network is using three interval inputs to model one interval target (outcome). The neural network has ten hidden units and one hidden layer. How many weights, including biases are being estimated?
You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:
A. 40
B. 41
C. 51
D. 50
Question 2
The importance of an input variable in predicting a target in an MLP-based neural network can be figured out by which of the following?
Response:
A. the highest absolute value of the parameter estimate between the input and any of the hidden neurons multiplied by the absolute value of the parameter estimate of the hidden neuron
B. none of the above
C. the highest absolute value of the parameter estimate between the input and any of the hidden neurons
D. the average of the absolute values of parameter estimates between the input and all of the hidden neurons
Question 3
Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
The percentage of observations correctly predicted in the validation data by the Ensemble model is in which of the following ranges?
Response:
A. 84-89.99%
B. 94% or higher
C. 90-93.99%-
D. less than 83.99%
Question 4
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. Mean Square Error
B. Total Profit
C. 1 - Misclassification Rate
D. Average Squared Error
Question 5
How many unique indicator variables were created during imputation?
Response:
A. 21 or higher
B. 11-20
C. 1-10
D. none
Solutions:
| Question 1 Answer: C | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: C | Question 5 Answer: B |
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