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

A00-226 real exams

Exam Code: A00-226

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

Updated: Jul 24, 2026

Q & A: 0 Questions and Answers

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Preparing for the SASInstitute A00-226 certification exam requires a solid understanding of SAS programming, data manipulation techniques, and statistical analysis. Candidates are recommended to have at least six months of experience in using SAS software and working with data analytics tools. A variety of study materials are available, including online courses, textbooks, and practice exams, to help candidates prepare for the exam.

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SASInstitute A00-226 exam is designed to test the candidate's understanding of SAS programming concepts and its applications in text analytics, time series analysis, experimentation, and optimization. A00-226 exam is composed of 70 multiple-choice and short-answer questions that must be answered within 110 minutes. The passing score for the exam is 70%.

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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. OPTMODEL procedure usage
        • 2. Formulating optimization problems
          Experimentation and Incremental Response Models20%- Experimental Design
          • 1. Design of Experiments (DOE) principles
            • 2. Factorial vs OFAT designs
              - 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
                      Time Series30%- Time Series Fundamentals
                      • 1. Stationarity and transformations
                        • 2. Components: trend, seasonality, noise, cycle
                          - Forecasting and Evaluation
                          • 1. Holdout sample evaluation
                            • 2. Forecast accuracy metrics and validation
                              - Time Series Modeling
                              • 1. Unobserved components models
                                • 2. ARIMA and ARIMAX models
                                  • 3. Exponential smoothing models
                                    Text Analytics30%- 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
                                            - Text Analytics Applications
                                            • 1. Text Rule Builder and SVD-based reduction
                                              • 2. Predictive modeling with text features

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