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

A00-226 real exams

Exam Code: A00-226

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

Updated: Jul 25, 2026

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SASInstitute A00-226 Exam Syllabus Topics:

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