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Our C_PAII10_35 guide torrent boosts 98-100% passing rate and high hit rate. Our SAP Certified Application Associate - SAP Predictive Analytics test torrent use the certificated experts and our questions and answers are chosen elaborately and based on the real exam according to the past years' exam papers and the popular trend in the industry. The language of our C_PAII10_35 study torrent is easy to be understood and the content has simplified the important information. Our product boosts the function to simulate the exam, the timing function and the self-learning and the self-assessment functions to make the learners master the C_PAII10_35 guide torrent easily and in a convenient way. Based on the plenty advantages of our product, you have little possibility to fail in the exam.
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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| Topic Areas | Topic Details, Courses, Books |
|---|---|
| Time Series with Modeler 8% - 12% | Build, debrief and apply a time-series model in Modeler. |
| 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. |
| 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. |
| 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. |
| Data Science supporting Automated Analytics 8% - 12% | Describe data partition strategies, data encoding, and interpretation of model curves. |
| Regression Modeling with Modeler < 8% | Build, debrief, save and apply a regression 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. |
| 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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