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

A00-226 real exams

Exam Code: A00-226

Exam Name: SAS Text Analytics, Time Series, Experimentation, and Optimization

Updated: Sep 12, 2026

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SASInstitute A00-226 Exam Syllabus Topics:

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