SAP C-PAII10-35 exam is intended to validate the candidate's knowledge and understanding of SAP Predictive Analytics. It covers a range of topics such as data preparation, modeling, and model deployment. C-PAII10-35 exam is designed to test the candidate's ability to design, implement and operate predictive analysis solutions using SAP Predictive Analytics. It also evaluates the candidate's understanding of the architecture and components of SAP Predictive Analytics and their ability to work with data sources, data models, and data visualization tools.
SAP C_PAII10_35 certification exam comprises 80 multiple-choice questions that must be completed in 180 minutes. C-PAII10-35 exam is computer-based and can be taken at any authorized SAP testing center. C-PAII10-35 exam covers a range of topics, including data preparation, predictive modeling, and model deployment. Candidates are expected to have a good understanding of statistical concepts, data mining techniques, and machine learning algorithms.
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To become an SAP Certified Application Associate - SAP Predictive Analytics, candidates must pass the C-PAII10-35 exam. This test measures the knowledge of the candidate in a wide range of topics such as predictive models, data sources, algorithms, and machine learning. Passing C-PAII10-35 exam demonstrates the candidate’s expertise in using SAP Predictive Analytics for decision-making.
SAP C-PAII10-35 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Basics of Automated Analytics | <8% | - Data storage and preparation - Handling missing values and outliers - Data types and variable roles |
| Regression Modeling with Modeler | 8%-12% | - Building regression models - Model validation and use cases - Interpreting regression outputs |
| Clustering with Automated Analytics | 8%-12% | - Building clustering models - Model debriefing and validation - Applying clustering results |
| Data Science Supporting Automated Analytics | 8%-12% | - Data partition strategies - Model curves and interpretation - Data encoding and transformation |
| Data Manager | <8% | - Data profiling and quality checks - Data transformation and preparation - Data import and connection |
| Classification Modeling with Modeler | 8%-12% | - Model performance evaluation - Building classification models - Deployment and application |
| Social and Recommendation Modeling | <8% | - Recommendation algorithms - Social network analysis basics |
| Time Series Modeling with Modeler | 8%-12% | - Building and adjusting time series models - Forecasting and result analysis - Time series concepts |
| Predictive Factory | >12% | - Key features and architecture - Creating and scheduling tasks - Deviation analysis and monitoring |
| Introduction to Predictive Analytics | >12% | - Key capabilities of SAP Predictive Analytics - Basic predictive modeling concepts - Use cases for predictive algorithms |
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