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

  • Exam Code: A00-226
  • Exam Name: SAS Text Analytics, Time Series, Experimentation, and Optimization
  • Updated: Sep 07, 2026     Q & A: 0 Questions and Answers

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The A00-226 exam is intended for professionals who have experience working with SAS software and want to validate their advanced analytics skills. To take A00-226 exam, candidates should have a solid understanding of statistical analysis and be familiar with SAS programming. A00-226 exam is ideal for data scientists, statisticians, and analysts who want to demonstrate their proficiency in advanced analytics techniques and tools.

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SASInstitute A00-226 exam covers a range of topics, including text analytics, time series analysis, experimental design, optimization, and simulation. A00-226 exam is divided into two sections, each consisting of 55 multiple-choice questions. Candidates are given 110 minutes to complete the exam. A00-226 exam is available in English, Japanese, and Korean languages.

SASInstitute A00-226 certification is ideal for professionals who work with large data sets and want to enhance their skills in SAS programming and data analysis. It is also suitable for data analysts, statisticians, and researchers who want to demonstrate their expertise in SAS software. SAS Text Analytics, Time Series, Experimentation, and Optimization certification provides an excellent opportunity for individuals to advance their careers, gain recognition in the industry, and increase their earning potential.

SASInstitute A00-226 is an industry-recognized certification exam in the field of analytics. A00-226 exam is designed to test the candidate's knowledge and skills related to SAS Text Analytics, Time Series, Experimentation, and Optimization. SAS Text Analytics, Time Series, Experimentation, and Optimization certification is ideal for professionals who want to demonstrate their expertise in the field of data analytics and stand out in the competitive job market.

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SASInstitute A00-226 Exam Syllabus Topics:

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

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