Exam Code: A00-274
Exam Name: Interactive Model Building using SAS Visual Statistics 8.4 on SAS Viya
Updated: Jul 26, 2026
Q & A: 0 Questions and Answers
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To prepare for the SASInstitute A00-274 exam, candidates are recommended to have a solid foundation in statistics, data mining, and machine learning concepts. They should also have experience using SAS software, particularly SAS Viya, and be familiar with programming languages such as SAS, Python, or R. SAS Institute provides various training courses and resources to help candidates prepare for the exam, including e-learning courses, virtual classes, and certification prep guides.
SASInstitute A00-274 exam is designed to test the knowledge and skills of candidates in interactive model building using SAS Visual Statistics 8.4 on SAS Viya. A00-274 exam is designed for individuals who want to validate their proficiency in using advanced statistical techniques to analyze complex data sets, build predictive models, and make informed business decisions.
SASInstitute A00-274 exam is ideal for professionals who work with data and want to build models that can be used to make informed decisions. A00-274 exam covers topics such as importing data, data exploration, data preparation, and model building. Candidates will be tested on their ability to use SAS Visual Statistics to build predictive models, create visualizations, and generate reports. A00-274 exam is designed to assess the candidate's proficiency in using SAS Viya to build models that are accurate, reliable, and scalable. Passing A00-274 exam will demonstrate the candidate's proficiency in using SAS Visual Statistics 8.4 on SAS Viya to build interactive models that can be used to make data-driven decisions.
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SAS Visual Statistics is a powerful tool for data exploration, analysis, and model building that allows users to create and deploy models interactively. The A00-274 certification exam covers all aspects of SAS Visual Statistics, including data preparation, model building, and deployment. Candidates are expected to demonstrate their knowledge of the tool's various features and how they can be utilized to solve real-world problems.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and Assessing Regression-type Models | 40% | - Model assessment
|
| Topic 2: Building and Assessing Segmentation Models | 32% | - Unsupervised segmentation
|
| Topic 3: Model Comparison and Scoring | 9% | - Model comparison
|
| Topic 4: SAS Visual Statistics Cross-functional Tasks | 18% | - Data preparation and transformation
|
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