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Worried about the A00-255 exam? Start with the free demo at Actual4dump: 87 practice questions for the A00-255 exam wait behind it, every answer verified, and the demo PDF downloads and prints freely so you can judge the SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 material before spending anything.

SASInstitute A00-255 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Sources20-25%- Prepare Source Data
  • 1. Modify source data
  • 2. Prepare data for predictive modeling
- Create and Explore Data Sources
  • 1. Create data sources from SAS tables in Enterprise Miner
  • 2. Explore and assess data sources
Topic 2: Predictive Model Assessment and Implementation25-30%- Model Evaluation
  • 1. Select appropriate fit statistics
  • 2. Use decision processing for oversampling adjustment
  • 3. Compare models using Model Comparison node
  • 4. Assess model performance using profit and loss information
- Model Deployment
  • 1. Score data sets within Enterprise Miner
Topic 3: Pattern Analysis10-15%- Segmentation and Association Analysis
  • 1. Perform sequence analysis and market basket analysis
  • 2. Identify clusters using Cluster and Segment Profile nodes
  • 3. Perform association analysis
Topic 4: Building Predictive Models35-40%- Model Development
  • 1. Build decision tree models
  • 2. Describe predictive modeling concepts and terminology
  • 3. Build regression models
  • 4. Build neural network models

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No mandatory prerequisite. Candidates should have hands-on experience with SAS Enterprise Miner and predictive modeling concepts.
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The official SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 outline assigns its weights like this:
  • Pattern Analysis (10-15%)
  • Data Sources (20-25%)
  • Predictive Model Assessment and Implementation (25-30%)
Budget your hours according to those weights and test each domain with the Actual4dump bank before exam day.

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

1. Create a project named Insurance, with a diagram named Explore.
2. Create the data source, DEVELOP, in SAS Enterprise Miner. DEVELOP is in the directory c:\workshop\Practice.
3. Set the role of all variables to Input, with the exception of the Target variable, Ins (1= has insurance, 0= does not have insurance).
4. Set the measurement level for the Target variable, Ins, to Binary.
5. Ensure that Branch and Res are the only variables with the measurement level of Nominal.
6. All other variables should be set to Interval or Binary.
7. Make sure that the default sampling method is random and that the seed is 12345.
The variable Branch has how many levels?
Response:

  • A. 12
  • B. 47
  • C. 8
  • D. 19
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
In the validation data, the lift corresponding to the fourth decile is in which of the following ranges?
Response:

  • A. 1.25-.49999
  • B. 1.5-1.74999
  • C. 1.75 or more
  • D. 0-1.24999
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Choose the correct statement that illustrates Decision Tree Split Search for continuous (interval) inputs:
Select one:
Response:

  • A. The variable goes through a binning process, the bins are weighted based on the proportion of events in each bin, and then finally tested as an optimal split point.
  • B. The variable goes through a non-linear transformation, and the transformed variable is used for testing.
  • C. Each unique value has the potential of being the optimal split point, except for the extreme observation.
  • D. Each unique value has the potential of being the optimal split point.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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:

  • A. 84.5212%
  • B. 83.1126%
  • C. 16.8874%
  • D. 85.2222%
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
The number of input variables being used by the Neural Network model is which of the following?
Response:

  • A. less than or equal to 10
  • B. 11-15
  • C. 16-20
  • D. 21 or more
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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