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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
Certificate Validity Period:No expiration
Available Languages:English
Real Exam Qty:55-60
Exam Price:USD 250
Related Certifications:SAS Certified Predictive Modeler Using SAS Enterprise Miner 14
SAS Certified Advanced Analytics Professional
SAS Certified Data Scientist
Exam Format:Multiple-choice, Short-answer, Performance-based
Exam Duration:165 minutes
Passing Score:725/1000
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
Topic 1: Building Predictive Models35-40%- Model Development
  • 1. Describe predictive modeling concepts and terminology
  • 2. Build regression models
  • 3. Build decision tree models
  • 4. Build neural network models
Topic 2: Predictive Model Assessment and Implementation25-30%- Model Evaluation
  • 1. Compare models using Model Comparison node
  • 2. Assess model performance using profit and loss information
  • 3. Select appropriate fit statistics
  • 4. Use decision processing for oversampling adjustment
- Model Deployment
  • 1. Score data sets within Enterprise Miner
Topic 3: Pattern Analysis10-15%- Segmentation and Association Analysis
  • 1. Perform association analysis
  • 2. Perform sequence analysis and market basket analysis
  • 3. Identify clusters using Cluster and Segment Profile nodes
Topic 4: Data Sources20-25%- Prepare Source Data
  • 1. Prepare data for predictive modeling
  • 2. Modify source data
- Create and Explore Data Sources
  • 1. Create data sources from SAS tables in Enterprise Miner
  • 2. Explore and assess data sources

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

1. For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:

A) It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
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 is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.


2. 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


3. 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.
In the training data set, consider only those observations for which the actual value of the target variable equals 1, TARGET=1. What percentage of these observations is being correctly predicted by the decision tree?
Response:

A) 15
B) 14.6216
C) 66.6667
D) 80.3571


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


5. An analyst is performing a market basket analysis (affinity analysis) on the purchase of Shaving Cream and Seltzer Water. The purchase data from a set of 250 customers is shown below:

What is the confidence of the rule "Shaving Cream implies Seltzer Water"? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

A) 67%
B) 57%
C) 60%
D) 40%


Solutions:

Question # 1
Answer: A
Question # 2
Answer: D
Question # 3
Answer: A
Question # 4
Answer: B
Question # 5
Answer: B

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