Exam Name: SAP Certified Application Associate - SAP Predictive Analytics
Certification Provider: SAP
Corresponding Certification: SAP Certified Application Associate
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Instant Download SAP : C_PAII10_35
Check Updated on: Jul 21, 2026
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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 |
|---|---|
| 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. |
| Data Science supporting Automated Analytics 8% - 12% | Describe data partition strategies, data encoding, and interpretation of model curves. |
| 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. |
| Clustering with Automated Analytics 8% - 12% | Build, debrief and apply a clustering model in Modeler. |
| Regression Modeling with Modeler < 8% | Build, debrief, save and apply a regression model in Modeler. |
| Time Series with Modeler 8% - 12% | Build, debrief and apply a time-series model in Modeler. |
| Basics of Automated Analytics < 8% | Identify different data types, storage and variable roles, as well as how to handle missing values and outliers. |
| Data Manager < 8% | Outline how to manipulate data in the Data Manager and how to use it to create dynamic data sets. |
| 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. |
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SAP C_PAII10_35 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Manager | <8% | - Data profiling and quality checks - Data import and connection - Data transformation and preparation |
| Topic 2: Regression Modeling with Modeler | 8%-12% | - Model validation and use cases - Interpreting regression outputs - Building regression models |
| Topic 3: Predictive Factory | >12% | - Deviation analysis and monitoring - Creating and scheduling tasks - Key features and architecture |
| Topic 4: Introduction to Predictive Analytics | >12% | - Use cases for predictive algorithms - Basic predictive modeling concepts - Key capabilities of SAP Predictive Analytics |
| Topic 5: Classification Modeling with Modeler | 8%-12% | - Building classification models - Model performance evaluation - Deployment and application |
| Topic 6: Clustering with Automated Analytics | 8%-12% | - Building clustering models - Model debriefing and validation - Applying clustering results |
| Topic 7: Time Series Modeling with Modeler | 8%-12% | - Time series concepts - Building and adjusting time series models - Forecasting and result analysis |
| Topic 8: Basics of Automated Analytics | <8% | - Data types and variable roles - Data storage and preparation - Handling missing values and outliers |
| Topic 9: Data Science Supporting Automated Analytics | 8%-12% | - Data encoding and transformation - Data partition strategies - Model curves and interpretation |
| Topic 10: Social and Recommendation Modeling | <8% | - Social network analysis basics - Recommendation algorithms |
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