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SASInstitute A00-226 exam is designed to test the candidate's knowledge and skills in SAS Text Analytics, Time Series, Experimentation, and Optimization. A00-226 exam is intended for individuals who are interested in utilizing SAS software for text analytics, time series analysis, experimentation, and optimization purposes. A00-226 exam comprises 60 multiple-choice questions that need to be completed within a time limit of 2 hours and 15 minutes.

SASInstitute A00-226 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Text Analytics30%- Text Analytics Applications
  • 1. Text Rule Builder and SVD-based reduction
    • 2. Predictive modeling with text features
      - Text Processing and Modeling
      • 1. Topic modeling and clustering
        • 2. Tokenization, parsing, and feature extraction
          - Text Mining Data Preparation
          • 1. Import and merge text and structured datasets
            • 2. Create and prepare text data sources for analysis
              Topic 2: Time Series30%- Time Series Modeling
              • 1. Exponential smoothing models
                • 2. ARIMA and ARIMAX models
                  • 3. Unobserved components models
                    - Time Series Fundamentals
                    • 1. Components: trend, seasonality, noise, cycle
                      • 2. Stationarity and transformations
                        - Forecasting and Evaluation
                        • 1. Holdout sample evaluation
                          • 2. Forecast accuracy metrics and validation
                            Topic 3: Experimentation and Incremental Response Models20%- Experimental Design
                            • 1. Factorial vs OFAT designs
                              • 2. Design of Experiments (DOE) principles
                                - 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
                                        Topic 4: Optimization20%- Advanced Optimization Techniques
                                        • 1. Data Envelopment Analysis (DEA)
                                          • 2. Constraints, decision variables, and parameter modeling
                                            - Linear Programming
                                            • 1. Formulating optimization problems
                                              • 2. OPTMODEL procedure usage

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