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

A00-226 actual test
  • Exam Code: A00-226
  • Exam Name: SAS Text Analytics, Time Series, Experimentation, and Optimization
  • Updated: Aug 17, 2026
  • Q & A: 0 Questions and Answers
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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. Formulating optimization problems
        • 2. OPTMODEL procedure usage
          Time Series30%- Time Series Fundamentals
          • 1. Components: trend, seasonality, noise, cycle
            • 2. Stationarity and transformations
              - Forecasting and Evaluation
              • 1. Forecast accuracy metrics and validation
                • 2. Holdout sample evaluation
                  - Time Series Modeling
                  • 1. Exponential smoothing models
                    • 2. ARIMA and ARIMAX models
                      • 3. Unobserved components models
                        Experimentation and Incremental Response Models20%- Experimental Design
                        • 1. Factorial vs OFAT designs
                          • 2. Design of Experiments (DOE) principles
                            - Incremental Response Modeling
                            • 1. Net Information Value (NIV) and WOE concepts
                              • 2. Treatment vs control group analysis
                                - Business Experimentation
                                • 1. Marketing campaign response modeling
                                  • 2. Nuisance variable control and blocking
                                    Text Analytics30%- Text Mining Data Preparation
                                    • 1. Import and merge text and structured datasets
                                      • 2. Create and prepare text data sources for analysis
                                        - 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

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