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To be eligible for the SAP C_PAII10_35 certification exam, candidates must have a minimum of one-year practical experience in using SAP Predictive Analytics software. Candidates are also advised to take the SAP Predictive Analytics course, which covers the topics tested in the exam. C_PAII10_35 course is available online and can be taken at any time.
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SAP C-PAII10-35 exam focuses on various topics such as understanding business requirements for predictive analytics, designing predictive models with SAP Predictive Analytics, preparing data for predictive modeling, visualizing and interpreting predictive model results, and deploying predictive models. Candidates who pass this certification exam become SAP Certified Application Associates in SAP Predictive Analytics.
By attaining the SAP C_PAII10_35 certification, professionals will gain a competitive advantage in the marketplace and be recognized as experts in the field of predictive analytics. SAP Certified Application Associate - SAP Predictive Analytics certification is ideal for data analysts, business analysts, and IT professionals who seek to enhance their skills in predictive analytics and enable their organizations to make data-driven decisions.
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SAP C_PAII10_35 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Predictive Factory | >12% | - Deviation analysis and monitoring - Key features and architecture - Creating and scheduling tasks |
| Topic 2: Basics of Automated Analytics | <8% | - Data storage and preparation - Handling missing values and outliers - Data types and variable roles |
| Topic 3: Clustering with Automated Analytics | 8%-12% | - Model debriefing and validation - Building clustering models - Applying clustering results |
| Topic 4: Regression Modeling with Modeler | 8%-12% | - Interpreting regression outputs - Building regression models - Model validation and use cases |
| Topic 5: Social and Recommendation Modeling | <8% | - Social network analysis basics - Recommendation algorithms |
| Topic 6: Data Science Supporting Automated Analytics | 8%-12% | - Data encoding and transformation - Data partition strategies - Model curves and interpretation |
| Topic 7: Classification Modeling with Modeler | 8%-12% | - Deployment and application - Building classification models - Model performance evaluation |
| Topic 8: Data Manager | <8% | - Data profiling and quality checks - Data transformation and preparation - Data import and connection |
| Topic 9: Time Series Modeling with Modeler | 8%-12% | - Time series concepts - Building and adjusting time series models - Forecasting and result analysis |
| Topic 10: Introduction to Predictive Analytics | >12% | - Key capabilities of SAP Predictive Analytics - Basic predictive modeling concepts - Use cases for predictive algorithms |
