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C_PAII10_35 Exam Certification Details:
| Languages: | English |
| Level: | Associate |
| Exam: | 80 questions |
| Sample Questions: | SAP C_PAII10_35 Exam Sample Question |
| Duration: | 180 mins |
| Cut Score: | 68% |
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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 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Basics of Automated Analytics | <8% | - Data types and variable roles - Data storage and preparation - Handling missing values and outliers |
| Time Series Modeling with Modeler | 8%-12% | - Building and adjusting time series models - Forecasting and result analysis - Time series concepts |
| Clustering with Automated Analytics | 8%-12% | - Applying clustering results - Model debriefing and validation - Building clustering models |
| Data Science Supporting Automated Analytics | 8%-12% | - Data encoding and transformation - Data partition strategies - Model curves and interpretation |
| Social and Recommendation Modeling | <8% | - Recommendation algorithms - Social network analysis basics |
| Classification Modeling with Modeler | 8%-12% | - Building classification models - Model performance evaluation - Deployment and application |
| Data Manager | <8% | - Data import and connection - Data transformation and preparation - Data profiling and quality checks |
| Predictive Factory | >12% | - Creating and scheduling tasks - Deviation analysis and monitoring - Key features and architecture |
| Introduction to Predictive Analytics | >12% | - Basic predictive modeling concepts - Use cases for predictive algorithms - Key capabilities of SAP Predictive Analytics |
| Regression Modeling with Modeler | 8%-12% | - Model validation and use cases - Building regression models - Interpreting regression outputs |
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