Welcome to your onestop solution for all the information you need to excel in the Predictive Modeling Using SAS Enterprise Miner 14 (A00255) Certification exam. This page provides an indepth overview of the SAS A00255 Exam Summary, Syllabus Topics, and Sample Questions, designed to lay the foundation for your exam preparation. We aim to help you achieve your SAS Certified Predictive Modeler Using SAS Enterprise Miner 14 certification goals seamlessly. Our detailed syllabus outlines each topic covered in the exam, ensuring you focus on the areas that matter most. With our sample questions and practice exams, you can gauge your readiness and boost your confidence to take on the SAS Predictive Modeling exam.
Why SAS Predictive Modeling Certification Matters
The SAS A00255 exam is globally recognized for validating your knowledge and skills. With the SAS Certified Predictive Modeler Using SAS Enterprise Miner 14 credential, you stand out in a competitive job market and demonstrate your expertise to make significant contributions within your organization. The Predictive Modeling Using SAS Enterprise Miner 14 Certification exam will test your proficiency in the various syllabus topics.
SAS A00255 Exam Summary:
Exam Name  Predictive Modeling Using SAS Enterprise Miner 14 
Exam Code  A00255 
Exam Duration  165 minutes 
Exam Questions  5560 
Passing Score  725 / 1000% 
Exam Price  $250 (USD) 
Books / Training 
Applied Analytics Using SAS Enterprise Miner Predictive Modeling with SAS Enterprise Miner 
Exam Registration  Pearson VUE 
Sample Questions  SAS Predictive Modeler Certification Sample Question 
Practice Exam  SAS Predictive Modeler Certification Practice Exam 
SAS A00255 Exam Syllabus Topics:
Objective  Details 

Data Sources  2025% 

Create data sources from SAS tables in Enterprise Miner 
 Use the Basic Metadata Advisor  Use the Advanced Metadata Advisor  Customize the Advanced Metadata Advisor  Set Role and Level meta data for data source variables  Set the Role of the table (raw, scoring, transactional, etc) 
Explore and assess data sources 
 Create and interpret plots, including Histograms, Pie charts, Scatter plot, Time series, Box plot  Identify distributions  Find outlying observations  Find number (or percent) of missing observations  Find levels of nominal variables  Explore associations between variables using plots by highlighting and selecting data  Compare balanced and actual response rates when oversampling has been performed  Explore data with the STAT EXPLORER node.  Explore input variable sample statistics  Browse data set observations (cases) 
Modify source data 
 Replace zero values with missing indicators using the REPLACEMENT node  Use the TRANFORMATION node to be able to correct problems with input data sources, such as variable distribution or outliers.  Use the IMPUTE node to impute missing values and create missing value indicators  Reduce the levels of a categorical variable  Use the FILTER node to remove cases 
Prepare data to be submitted to a predictive model 
 Select a portion of a data set using the SAMPLE node  Partition data with the PARTITION Node  Use the VARIABLE SELECTION node to identify important variables to be included in a predictive model.  Use the PARTIAL LEAST SQUARES node to identify important variables to be included in a predictive model.  Use a DECISION TREE or REGRESSION nodes to identify important variables to be included in a predictive model. 
Building Predictive Models  3540% 

Describe key predictive modeling terms and concepts 
 Data partitioning: training, validation, test data sets  Observations (cases), independent (input) variables, dependent (target) variables  Measurement scales: Interval, ordinal, nominal (categorical), binary variables  Prediction types: decisions, rankings, estimates  Dimensionality, redundancy, irrelevancy  Decision trees, neural networks, regression models  Model optimization, overfitting, underfitting, model selection  Describe ensemble models 
Build predictive models using decision trees 
 Explain how decision trees identify split points  Build decision trees in interactive mode  Change splitting rules  Explain how missing values can be handled by decision trees  Assess probability using a decision tree  Prune decision trees  Adjust properties of the DECISION TREE node, including: subtree method, Number of Branches, Leaf Size, Significance Level, Surrogate Rules, Bonferroni Adjustment  Interpret results of the decision tree node, including: trees, leaf statistics, treemaps, score rankings overlay, fit statistics, output, variable importance, subtree assessment plots  Explore model output (exported) data sets 
Build predictive models using regression 
 Explain the relationship between target variable and regression technique  Explain linear regression  Explain logistic regression (Logit link function, maximum likelihood)  Explain the impact of missing values on regression models  Select inputs for regression models using forward, backward, stepwise selection techniques  Adjust thresholds for including variables in a model  Interpret a logistic regression model using log odds  Interpret the results of a REGRESSION node (Output, Fit Statistics, Score Ranking Overlay charts)  Use fit statistics and iteration plots to select the optimum regression model for different decision types  Add polynomial regression terms to regression models.  Determine when to add polynomial terms to linear regression models. 
Build predictive models using neural networks 
 Theory of neural networks (Hidden units, Tanh function, bias vs intercept, variable standardization)  Build a neural network model  Use regression models to select inputs for a neural network  Explain how neural networks optimize their model (stopped training)  Recognize overfit neural network models.  Interpret the results of a NEURAL NETWORK node, including: Output, Fit Statistics, Iteration Plots, and Score Rankings Overlay charts 
Predictive Model Assessment and Implementation  2530% 

Use the correct fit statistic for different prediction types 
 Misclassification  Average Square Error  Profit/Loss  Other standard model fit statistics 
Use decision processing to adjust for oversampling (separate sampling) 
 Explain reasons for oversampling data  Adjust prior probabilities 
Use profit/loss information to assess model performance 
 Build a profit/loss matrix  Add a profit/loss matrix to a predictive model  Determine an appropriate value to use for expected profit/loss for primary outcome  Optimize models based on expected profit/loss 
Compare models with the MODEL COMPARISON node 
 Model assessment statistics  ROC Chart  Score Rankings Chart, including (cumulative) % response chart, (cumulative) Lift chart, gains chart.  Total expected profit  Effect of oversampling 
Score data sets within Enterprise Miner 
 Configure a data set to be scored in Enterprise Miner  Use the SCORE node to score new data  Save scored data to an external location with the SAVE DATA node  Export SAS score code 
Pattern Analysis  1015% 

Identify clusters of similar data with the CLUSTER and SEGMENT PROFILE nodes 
 Select variables to use to define the clusters  Standardize variable scales  Explore clusters with results output and plots  Compare distribution of variables within clusters 
Perform association and sequence analysis (market basket analysis) 
 Explain association concepts (Support, confidence, expected confidence, lift, difference between association and sequence rules)  Create a data set for association analysis  Interpret the results and graphs of the ASSOCIATION node. 
The SAS has created this credential to assess your knowledge and understanding in the specified areas through the A00255 certification exam. The SAS Certified Predictive Modeler Using SAS Enterprise Miner 14 exam holds significant value in the market due to the brand reputation of SAS. We highly recommend thorough study and extensive practice to ensure you pass the Predictive Modeling Using SAS Enterprise Miner 14 exam with confidence.