Exam ID: A00-226
Exam Name: SAS Text Analytics, Time Series, Experimentation and Optimization
Successful candidates should have hands-on experience with a variety of SAS data preparation tools, including experience with the following analytical tools:
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- SAS Text Analytics
- SAS/ETS
- SAS/OR
SAS A00-226 Exam Summary:
Exam Name | SAS Text Analytics, Time Series, Experimentation and Optimization |
Exam Code | A00-226 |
Exam Duration | 110 minutes |
Exam Questions | 50-55 multiple choice or short answer |
Passing Score | 68 |
Exam Price | $180 (USD) |
Books | 1. SAS Academy for Data Science: Advanced Analytics 2. Text Analytics Using SAS Text Miner 3. Time Series Modeling Essentials 4. Experimentation in Data Science 5. Building and Solving Optimization Models with SAS/OR |
Sample Questions | SAS Advanced Analytics Professional Certification Sample Question |
Practice Exam | SAS Advanced Analytics Professional Certification Practice Exam |
SAS A00-226 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 non-text 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 trade-off between predictive power and interpretability – Set up Text Cluster and Text Topic nodes to affect this trade-off – Perform predictive modeling using the Text Rule Builder node |
Use the High-Performance (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 goodness-of-fit 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 hold-out 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 one-factor-at-a-time (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 |