Exam Name: SAP Certified Application Associate - SAP Predictive Analytics
Certification Provider: SAP
Corresponding Certification: SAP Certified Application Associate
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SAP Certified Application Associate - SAP Predictive Analytics: C-PAII10-35 Exam Premium File as PDF & Test Engine
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Last Updated: Jul 02, 2026
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C-PAII10-35 Exam Certification Details:
| Duration: | 180 mins |
| Cut Score: | 68% |
| Exam: | 80 questions |
| Level: | Associate |
| Languages: | English |
| Sample Questions: | SAP C-PAII10-35 Exam Sample Question |
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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.
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SAP C-PAII10-35 Certification Exam Topics:
| Topic Areas | Topic Details, Courses, Books |
|---|---|
| Basics of Automated Analytics < 8% | Identify different data types, storage and variable roles, as well as how to handle missing values and outliers. |
| Time Series with Modeler 8% - 12% | Build, debrief and apply a time-series model in Modeler. |
| 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. |
| 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. |
| 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. |
| Regression Modeling with Modeler < 8% | Build, debrief, save and apply a regression model in Modeler. |
| Classification Modeling with Modeler > 12% | Build and apply classification models in Modeler, and implement deviation analysis. |
| Social and Recommendation < 8% | Build a social recommendation and analysis. |
| Data Science supporting Automated Analytics 8% - 12% | Describe data partition strategies, data encoding, and interpretation of model curves. |
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