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SASInstitute A00-255 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building Predictive Models	35–40%	- Build models using neural networks - Build models using regression techniques - Understand predictive modeling concepts - Build models using decision trees
Topic 2: Predictive Model Assessment and Implementation	25–30%	- Evaluate performance via profit/loss and comparison - Adjust for oversampling and sampling methods - Apply appropriate fit statistics - Score and deploy models
Topic 3: Data Sources	20–25%	- Explore and assess data sources - Modify and prepare source data for modeling - Create data sources from SAS tables
Topic 4: Pattern Analysis	10–15%	- Identify clusters and segments - Interpret pattern discovery results

SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:

1. If we were to add a Transformation node, what would be the default transformation for interval inputs for the present scenario?
Response:

A) Maximum Correlation
B) Maximum Normal
C) none of the above
D) Optimal

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) 86.25
B) 690
C) 6.9
D) 69

3. 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) the average of the absolute values of parameter estimates between the input and all of the hidden neurons
C) none of the above
D) the highest absolute value of the parameter estimate between the input and any of the hidden neurons

4. Suppose your input variables have missing values. Before running a decision tree with these input variables, you should do which of the following?
Response:

A) impute only class variables using the Tree method but do not impute the interval variables
B) impute all missing values using the Tree method
C) not impute any missing values because trees can handle them.
D) impute only interval variables using the Tree method but do not impute the class variables

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 percentage of all observations is being correctly predicted in the test data set by the decision tree?
Response: