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SASInstitute A00-226 Questions & Answers - in .pdf

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  • Vendor: SASInstitute
  • Exam Code: A00-226
  • Exam Name: SAS Text Analytics, Time Series, Experimentation, and Optimization
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A00-226 Study Guide
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  • Update: Sep 11, 2026
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  • Vendor: SASInstitute
  • Exam Code: A00-226
  • Exam Name: SAS Text Analytics, Time Series, Experimentation, and Optimization
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SASInstitute A00-226 Exam Overview:

Certification Vendor:SAS Institute
Exam Name:SAS Text Analytics, Time Series, Experimentation, and Optimization
Exam Number:A00-226
Passing Score:68%
Exam Price:$180 USD
Exam Duration:110 minutes
Related Certifications:Predictive Modeling Using SAS Enterprise Miner 14 (A00-255)
SAS Advanced Predictive Modeling (A00-225)
Available Languages:English
Exam Format:Multiple choice, Interactive performance-based questions, Short answer
Certificate Validity Period:Does not expire (once earned)
Real Exam Qty:50–55
Recommended Training:SAS Advanced Analytics Professional Prep
SAS Text Analytics Training (SAS Learning)
Exam Registration:Pearson VUE SAS Exams
SAS Certification Registration Portal
Exam Way:Online or onsite via Pearson VUE testing centers
Pre Condition:Recommended: strong SAS programming knowledge and experience with SAS/ETS, SAS Text Analytics, and SAS/OR. No strict formal prerequisite.
Official Syllabus URL:https://www.sas.com/en_us/certification/credentials/advanced-analytics/advanced-analytics-professional/text-analytics-exam.html

SASInstitute A00-226 Exam Syllabus Topics:

SectionWeightObjectives
Optimization20%- Advanced Optimization Techniques
  • 1. Constraints, decision variables, and parameter modeling
    • 2. Data Envelopment Analysis (DEA)
      - Linear Programming
      • 1. OPTMODEL procedure usage
        • 2. Formulating optimization problems
          Time Series30%- Forecasting and Evaluation
          • 1. Holdout sample evaluation
            • 2. Forecast accuracy metrics and validation
              - Time Series Fundamentals
              • 1. Components: trend, seasonality, noise, cycle
                • 2. Stationarity and transformations
                  - Time Series Modeling
                  • 1. ARIMA and ARIMAX models
                    • 2. Unobserved components models
                      • 3. Exponential smoothing models
                        Text Analytics30%- Text Analytics Applications
                        • 1. Text Rule Builder and SVD-based reduction
                          • 2. Predictive modeling with text features
                            - Text Processing and Modeling
                            • 1. Topic modeling and clustering
                              • 2. Tokenization, parsing, and feature extraction
                                - Text Mining Data Preparation
                                • 1. Import and merge text and structured datasets
                                  • 2. Create and prepare text data sources for analysis
                                    Experimentation and Incremental Response Models20%- Business Experimentation
                                    • 1. Marketing campaign response modeling
                                      • 2. Nuisance variable control and blocking
                                        - Incremental Response Modeling
                                        • 1. Net Information Value (NIV) and WOE concepts
                                          • 2. Treatment vs control group analysis
                                            - Experimental Design
                                            • 1. Factorial vs OFAT designs
                                              • 2. Design of Experiments (DOE) principles
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