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

"SAS Text Analytics, Time Series, Experimentation, and Optimization", also known as A00-226 exam, is a SASInstitute Certification. With the complete collection of questions and answers, PrepAwayPDF has assembled to take you through 0 Q&As to your A00-226 Exam preparation. In the A00-226 exam resources, you will cover every field and category in SASInstitute Certification Certification helping to ready you for your successful SASInstitute Certification.

  • Exam Code: A00-226
  • Exam Name: SAS Text Analytics, Time Series, Experimentation, and Optimization
  • Certification Provider: SASInstitute
  • Corresponding Certification: SASInstitute Certification
  • Updated: Sep 11, 2026
  • No. of Questions: 0 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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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
      - Forecasting and Evaluation
      • 1. Forecast accuracy metrics and validation
        • 2. Holdout sample evaluation
          - Time Series Modeling
          • 1. ARIMA and ARIMAX models
            • 2. Unobserved components models
              • 3. Exponential smoothing models
                Experimentation and Incremental Response Models20%- 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
                        - Incremental Response Modeling
                        • 1. Treatment vs control group analysis
                          • 2. Net Information Value (NIV) and WOE concepts
                            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. Tokenization, parsing, and feature extraction
                                  • 2. Topic modeling and clustering
                                    - Text Analytics Applications
                                    • 1. Text Rule Builder and SVD-based reduction
                                      • 2. Predictive modeling with text features
                                        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

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