This page is a onestop solution for any information you may require for SAS Text Analytics, Time Series, Experimentation and Optimization (A00226) Certification exam. The SAS A00226 Exam Summary, Syllabus Topics and Sample Questions provide the base for the actual SAS Certified Advanced Analytics Professional Using SAS 9 exam preparation, we have designed these resources to help you get ready to take your dream exam.
The SAS Text Analytics, Time Series, Experimentation and Optimization credential is globally recognized for validating SAS Advanced Analytics Professional knowledge. With the SAS Certified Advanced Analytics Professional Using SAS 9 Certification credential, you stand out in a crowd and prove that you have the SAS Advanced Analytics Professional knowledge to make a difference within your organization. The SAS Text Analytics, Time Series, Experimentation and Optimization Certification (A00226) exam will test the candidate's knowledge on following areas.
SAS A00226 Exam Summary:
Exam Name  SAS Text Analytics, Time Series, Experimentation and Optimization 
Exam Code  A00226 
Exam Duration  110 minutes 
Exam Questions  5055 multiple choice or short answer 
Passing Score  68 
Exam Price  $180 (USD) 
Training  
Exam Registration  Pearson VUE 
Sample Questions  SAS Advanced Analytics Professional Certification Sample Question 
Practice Exam  SAS Advanced Analytics Professional Certification Practice Exam 
SAS A00226 Exam Topics:
Objective  Details 

Text Analytics  30% 

Create data sources for text mining 
 Create data sources that can be used by SAS Enterprise Miner Projects  Identify data sources that are relevant for text mining 
Import data into SAS Text Analytics 
 Process document collections and create a single SAS data set for text mining using the Text Import Node  Merge a SAS data set created from Text Importer with another SAS data set containing target information and other nontext variables  Compare two models, one using only conventional input variables and another using the conventional inputs and some text mining variables 
Use text mining to support forensic linguistics using stylometry techniques  
Retrieve information for Analysis 
 Use the Interactive Text Filter Viewer for information retrieval  Use the Medline medical abstracts data for information retrieval 
Parse and quantify Text 
 Provide guidelines for using weights  Use SVD to project documents and terms into a smaller dimension metric space  Discuss Text Topic and Text Cluster results in light of the SVD 
Perform predictive modeling on text data 
 Explain the tradeoff between predictive power and interpretability  Set up Text Cluster and Text Topic nodes to affect this tradeoff  Perform predictive modeling using the Text Rule Builder node 
Use the HighPerformance (HP) Text Miner Node 
 Identify the benefits of the HP Text Miner node  Use the HPTMINE procedure 
Time Series  30% 

Identify and define time series characteristics, components and the families of time series models 
 Transform transactional data into time series data (Accumulate) using PROC TIMESERIES Transactional Data Accumulation and Time Binning  Define the systematic components in a time series (level, seasonality, trend, irregular, exogenous, cycle)  Describe the decomposition of time series variation (noise and signal)  List three families of time series models exponential smoothing (ESM) autoregressive integrated moving average with exogenous variables (ARIMAX) unobserved components (UCM)  Identify the strengths and weaknesses of the three model types usability complexity robustness ability to accommodate dynamic regression effects 
Diagnose, fit and interpret ARIMAX Models 
 Analyze a time series with respect to signal (system variation) and noise (random variation)  Explain the importance of the Autocorrelation Function Plot and the White Noise Test in ARMA modeling  Compare and contrast ARMA and ARIMA models  Define a stationary time series and discuss its importance  Describe and identify autoregressive and moving average processes  Estimate an order 1 autoregressive model  Evaluate estimates and goodnessoffit statistics  Explain the X in ARMAX  Relate linear regression with time series regression models  Recognize linear regression assumptions  Explain the relationship between ordinary multiple linear regression models and time series regression models  Explain how to use a holdout sample to forecast  Given a scenario, use model statistics to evaluate forecast accuracy  Given a scenario, use sample time series data to exemplify forecasting concepts 
Diagnose, fit and interpret Exponential Smoothing Models 
 Describe the history of ESM  Explain how ESMs work and the types of systematic components they accommodate  Describe each of the seven types of ESM formulas  Given a sample data set, choose the best ESM using a holdout sample, output fit statistics, and forecast data sets 
Diagnose, fit and interpret Unobserved Components Models 
 Describe the basic component models: level, slope, seasonal  Be able to explain UCM strengths and when it would be good to use UCM Example: Visualization of component variation  Given a sample scenario, be able to explain how you would build a UCM Adding and deleting component models and interpreting the diagnostics 
Experimentation & Incremental Response Models  20% 

Explain the role of experiments in answering business questions 
 Determine whether a business question should be answered with a statistical model  Compare observational and experimental data  List the considerations for designing an experiment  Control the experiment for nuisance variables  Explain the impact of nuisance variables on the results of an experiment  Identify the benefits of deploying an experiment on a small scale 
Relate experimental design concepts and terminology to business concepts and terminology 
 Define Design of Experiments (DOE) terms (response, factor, effect, blocking, etc)  Map DOE terms to business marketing terms  Define and interpret interactions between factors  Compare onefactoratatime (OFAT) experiment methods to factorial methods  Describe the attributes of multifactor experiments (randomization, orthogonality, etc)  Identify effects in a multifactor experiment  Explain the difference between blocks and covariates 
Explain how incremental response models can identify cases that are most responsive to an action 
 Design the experimental structure to assess the impact of the model versus the impact of the treatment  Explain the effect of both the model and the message from assessment experiment data  Describe the standard customer segments with respect to marketing campaign targets  Explain the value of using control groups in data science  Define an incremental response 
Use the Incremental Response node in SAS Enterprise Miner 
 List the required data structure components of the Incremental Response node  Explain Net Information Value (NIV) and Penalized Net Information Value (PNIV) and their use in SAS Enterprise Miner  Explain Weight of Evidence (WOE) and Net Weight of Evidence (NWOE) and their use in SAS Enterprise Miner  Use stepwise regression with the Incremental Response node  Adjust model properties for various types of incremental revenue analysis  Compare variable/constant revenue and cost models  Understand and explain the value of difference scores in the combined incremental response model  Use difference scores to compare treatment and control 
Optimization  20% 

Optimize linear programs 
 Explain local properties of functions that are used to solve mathematical optimization problems  Use the OPTMODEL procedure to enter and solve simple linear programming problems  Formulate linear programming problems using index sets and arrays of decision variables, families of constraints, and values stored in parameter arrays  Modify a linear programming problem (changing bounds or coefficients, fixing variables, adding variables or constraints) within the OPTMODEL procedure  Use the Data Envelope Analysis (DEA) linear programming technique 
Optimize nonlinear programs 
 Describe how, conceptually and geometrically, iterative improvement algorithms solve nonlinear programming problems  Identify the optimality conditions for nonlinear programming problems  Solve nonlinear programming problems using the OPTMODEL procedure  Interpret information written to the SAS log during the solution of a nonlinear programming problem  Differentiate between the NLP algorithms and how solver options influence the NLP algorithms 
The SAS has created this credential to assess the knowledge and understanding of a candidate in the area as above via the certification exam. The SAS Advanced Analytics Professional (A00226) Certification exam contains a high value in the market being the brand value of the SAS attached with it. It is highly recommended to a candidate to do a thorough study and also get a hand full of the practice to clear SAS Text Analytics, Time Series, Experimentation and Optimization exam without any hiccups.