C_PAII10_35 Exam Certification Details:
| Level: | Associate |
| Cut Score: | 68% |
| Exam: | 80 questions |
| Languages: | English |
| Sample Questions: | SAP C_PAII10_35 Exam Sample Question |
| Duration: | 180 mins |
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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.
SAP C_PAII10_35 Certification Exam Topics:
| Topic Areas | Topic Details, Courses, Books |
|---|---|
| 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. |
| Social and Recommendation < 8% | Build a social recommendation and analysis. |
| Clustering with Automated Analytics 8% - 12% | Build, debrief and apply a clustering model in Modeler. |
| Time Series with Modeler 8% - 12% | Build, debrief and apply a time-series model in Modeler. |
| Data Science supporting Automated Analytics 8% - 12% | Describe data partition strategies, data encoding, and interpretation of model curves. |
| 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. |
| Classification Modeling with Modeler > 12% | Build and apply classification models in Modeler, and implement deviation analysis. |
| 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. |
| Regression Modeling with Modeler < 8% | Build, debrief, save and apply a regression model in Modeler. |
SAP C_PAII10_35 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Introduction to Predictive Analytics | 12% | - Key features of SAP Predictive Analytics - Fundamental predictive analytics concepts |
| Topic 2: Classification Modeling with Modeler | 12% | - Model evaluation and application - Classification model building |
| Topic 3: Clustering with Automated Analytics | 8% - 12% | - Clustering techniques and application - Analyzing cluster results |
| Topic 4: Data Manager | <8% | - Data manipulation and dataset creation - Scheduling and task management |
| Topic 5: Data Science Supporting Automated Analytics | 8% - 12% | - Role of data science in predictive analytics - Data pre-processing |
| Topic 6: Time Series with Modeler | 8% - 12% | - Forecast analysis - Time series model creation |
| Topic 7: Regression Modeling with Modeler | <8% | - Regression model development - Validation and application of regression models |
| Topic 8: Social and Recommendation | <8% | - Recommendation model usage - Social data analysis concepts |
| Topic 9: Predictive Factory | 12% | - Predictive Factory architecture and components - Model deployment and management |
| Topic 10: Basics of Automated Analytics | <8% | - Automated analytics core capabilities - Operational workflows |
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