The A00-226 certification exam is a challenging assessment that requires extensive preparation and study. Candidates must have a solid understanding of SAS programming concepts, data manipulation, and statistical analysis techniques. They must also have hands-on experience in using SAS tools to perform advanced analytics. To prepare for the exam, candidates can take online courses, attend training workshops, and practice with sample questions and exercises.
SASInstitute A00-226 exam is a comprehensive exam that tests the candidate's knowledge and skills in SAS software. A00-226 exam is designed to assess the candidate's ability to use SAS software to analyze text data, create time series models, design experiments, and optimize solutions. Candidates who pass the SASInstitute A00-226 exam demonstrate their ability to use SAS software to solve complex data analysis problems and are recognized as experts in SAS Text Analytics, Time Series, Experimentation, and Optimization.
To prepare for the SASInstitute A00-226 exam, candidates can enroll in online training courses or attend classroom training sessions. The training courses cover all the topics included in the exam and provide candidates with hands-on experience in using SAS software for data analysis. Candidates can also access study materials and practice tests to assess their knowledge and identify areas that need improvement.
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SASInstitute A00-226 certification exam consists of 70 multiple choice and short-answer questions that assess the candidate’s knowledge of SAS tools, techniques, and best practices for text analytics, time series analysis, experimentation, and optimization. Candidates have 2 hours and 15 minutes to complete the exam. They must achieve a minimum score of 70% to obtain the certification.
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SASInstitute A00-226 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Text Analytics | 30% | - Create data sources for text mining
- Use High-Performance Text Miner
- Perform predictive modeling on text data
- Parse and quantify text
- Import data into SAS Text Analytics
|
| Topic 2: Optimization | 20% | - Optimize nonlinear programs
- Optimize linear programs
|
| Topic 3: Experimentation & Incremental Response Models | 20% | - Relate design terminology to business contexts
- Explain experimental design concepts
- Use Incremental Response node in SAS Enterprise Miner
- Use incremental response models to identify responsive cases
|
| Topic 4: Time Series | 30% | - Fit and interpret exponential smoothing models
- Fit and interpret unobserved components models
- Fit and interpret ARIMAX models
- Identify time series characteristics and components
|