C-PAII10-35 Exam Certification Details:
| Cut Score: | 68% |
| Languages: | English |
| Level: | Associate |
| Exam: | 80 questions |
| Duration: | 180 mins |
| Sample Questions: | SAP C-PAII10-35 Exam Sample Question |
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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. |
| Classification Modeling with Modeler > 12% | Build and apply classification models in Modeler, and implement deviation analysis. |
| Data Science supporting Automated Analytics 8% - 12% | Describe data partition strategies, data encoding, and interpretation of model curves. |
| Time Series with Modeler 8% - 12% | Build, debrief and apply a time-series 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. |
| Social and Recommendation < 8% | Build a social recommendation and analysis. |
| Regression Modeling with Modeler < 8% | Build, debrief, save and apply a regression model in Modeler. |
| Clustering with Automated Analytics 8% - 12% | Build, debrief and apply a clustering 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. |
SAP C-PAII10-35 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Building | - Time series forecasting - Unsupervised learning models - Supervised learning models |
| Topic 2: Model Evaluation and Validation | - Performance metrics - Model accuracy assessment |
| Topic 3: Business Applications | - Use cases in finance, sales, and operations - Decision automation scenarios |
| Topic 4: Data Preparation | - Data cleansing and transformation - Data integration and connectivity |
| Topic 5: Deployment and Integration | - Deploying predictive models - Integration with SAP systems |
| Topic 6: Introduction to SAP Predictive Analytics | - SAP Predictive Analytics architecture and components - Overview of predictive analytics concepts |
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