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C-PAII10-35 Exam Certification Details:
| Sample Questions: | SAP C-PAII10-35 Exam Sample Question |
| Cut Score: | 68% |
| Exam: | 80 questions |
| Duration: | 180 mins |
| Languages: | English |
| Level: | Associate |
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SAP C-PAII10-35 Exam Description:
This certification exam validates that the candidate possesses the fundamental and core knowledge required of the consultant profile. This certification proves that the candidate has an overall understanding and in‐depth technical skills to participate as a member of a project team in a mentored role. This certification exam is recommended as an entry level qualification to allow consultants to get acquainted with the fundamentals of SAP Predictive Analytics including how the various capabilities of SAP Predictive Analytics provide insights into specific business predictive analytics challenges.
Our real exam test (SAP Certified Application Associate - SAP Predictive Analytics) types introduce
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SAP C-PAII10-35 Certification Exam Topics:
| Topic Areas | Topic Details, Courses, Books |
|---|---|
| Predictive Factory > 12% | Describe the key features of SAP Predictive Factory, including, but not limited to: building time series models, using classification modeling, using regression modeling, creating and scheduling tasks, and using deviation analysis. |
| Time Series with Modeler 8% - 12% | Build, debrief and apply a time-series model in Modeler. |
| Introduction to Predictive Analytics > 12% | Describe basic predictive modeling concepts, identify use cases for predictive algorithms, and outline the key capabilities of SAP Predictive Analytics. |
| Social and Recommendation < 8% | Build a social recommendation and analysis. |
| Clustering with Automated Analytics 8% - 12% | Build, debrief and apply a clustering model in Modeler. |
| Data Manager < 8% | Outline how to manipulate data in the Data Manager and how to use it to create dynamic data sets. |
| Basics of Automated Analytics < 8% | Identify different data types, storage and variable roles, as well as how to handle missing values and outliers. |
| Classification Modeling with Modeler > 12% | Build and apply classification models in Modeler, and implement deviation analysis. |
| Data Science supporting Automated Analytics 8% - 12% | Describe data partition strategies, data encoding, and interpretation of model curves. |
| Regression Modeling with Modeler < 8% | Build, debrief, save and apply a regression model in Modeler. |
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