SASInstitute A00-226 real exam prep : SAS Text Analytics, Time Series, Experimentation, and Optimization

  • Exam Code: A00-226
  • Exam Name: SAS Text Analytics, Time Series, Experimentation, and Optimization
  • Updated: Aug 26, 2026
  • Q&As: 0 Questions and Answers

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SASInstitute A00-226 Exam Syllabus Topics:

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

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