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SASInstitute A00-255 Exam Overview:

Certification Vendor:SAS Institute
Exam Name:SAS Predictive Modeling Using SAS Enterprise Miner 14
Exam Number:A00-255
Available Languages:English
Exam Duration:165 minutes
Real Exam Qty:55-60
Exam Price:USD 250
Exam Format:Multiple-choice, Short-answer, Performance-based
Related Certifications:SAS Certified Predictive Modeler Using SAS Enterprise Miner 14
SAS Certified Advanced Analytics Professional
SAS Certified Data Scientist
Passing Score:725/1000
Certificate Validity Period:No expiration
Sample Questions:SASInstitute A00-255 Sample Questions
Exam Way:Computer-based exam delivered through Pearson VUE testing centers and online proctored testing.
Pre Condition:No mandatory prerequisite. Candidates should have hands-on experience with SAS Enterprise Miner and predictive modeling concepts.
Official Syllabus URL:https://www.sas.com/en_us/certification/credentials/advanced-analytics/predictive-modeler.html

SASInstitute A00-255 Exam Syllabus Topics:

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

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

1. The number of hidden layers in this Neural Network model is which of the following?
Response:

A) 1
B) 3
C) 4 or more
D) 2


2. If the bank wanted to select the best model based on which model achieves the highest lift for the third decile in the validation data, then which of the following is the best model?
Response:

A) Decision Tree
B) Decision Tree (3-way)
C) Neural Network
D) Regression


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.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:

A) Decision Tree
B) Neural Network
C) Ensemble
D) Regression


4. You are building a model to identify fraud. Your model will produce predictions that can be interpreted as the probability of fraud. You will pass on the top 100 scoring cases to management for investigation. Assume that you have sufficient data to hold out a validation and test data set for model evaluation.
Which selection would represent a reasonable ordering of fit statistics (best to worst) for this situation?
Select one:
Response:

A) Misclassification rate, ROC index, ASE
B) R-square, AIC, K-S statistic
C) ROC index, ASE, misclassification rate
D) ASE, Lift for the top 10%, ROC index


5. Sometimes in predictive modeling we build models using a sample that has a primary outcome proportion different from true population proportion. This is usually done when the ratio of primary to secondary outcomes in a binary target variable in the population is close to which of the following?
Response:

A) 0.05
B) 1.2
C) 1
D) 0.8


Solutions:

Question # 1
Answer: A
Question # 2
Answer: A
Question # 3
Answer: C
Question # 4
Answer: C
Question # 5
Answer: A

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