Online Version
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SAP C-PAII10-35 Certification Exam Topics:
| Topic Areas | Topic Details, Courses, Books |
|---|---|
| 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. |
| Data Manager < 8% | Outline how to manipulate data in the Data Manager and how to use it to create dynamic data sets. |
| Time Series with Modeler 8% - 12% | Build, debrief and apply a time-series model in Modeler. |
| Social and Recommendation < 8% | Build a social recommendation and 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. |
| Data Science supporting Automated Analytics 8% - 12% | Describe data partition strategies, data encoding, and interpretation of model curves. |
| Clustering with Automated Analytics 8% - 12% | Build, debrief and apply a clustering model in Modeler. |
| Classification Modeling with Modeler > 12% | Build and apply classification models in Modeler, and implement deviation analysis. |
| Basics of Automated Analytics < 8% | Identify different data types, storage and variable roles, as well as how to handle missing values and outliers. |
Comprehensive Version and Good Service
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PDF Version
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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.
Software Version
The software version of our C-PAII10-35 study engine is designed to simulate a real exam situation. You can install it to as many computers as you need as long as the computer is in Windows system. And our software of the C-PAII10-35 training material also allows different users to study at the same time. It's economical for a company to buy it for its staff. Friends or workmates can also buy and learn with it together. With our software of C-PAII10-35 guide exam: SAP Certified Application Associate - SAP Predictive Analytics, you can practice and test yourself just like you are in a real exam. The results of your test will be analyzed and a statistics will be presented to you. So you can see how you have done and know which kinds of questions of the C-PAII10-35 exam are to be learned more.
C-PAII10-35 Exam Certification Details:
| Exam: | 80 questions |
| Cut Score: | 68% |
| Duration: | 180 mins |
| Languages: | English |
| Level: | Associate |
| Sample Questions: | SAP C-PAII10-35 Exam Sample Question |
SAP C-PAII10-35 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Regression Modeling with Modeler | 8%-12% | - Building regression models - Interpreting regression outputs - Model validation and use cases |
| Classification Modeling with Modeler | 8%-12% | - Building classification models - Deployment and application - Model performance evaluation |
| Clustering with Automated Analytics | 8%-12% | - Applying clustering results - Building clustering models - Model debriefing and validation |
| Basics of Automated Analytics | <8% | - Data storage and preparation - Data types and variable roles - Handling missing values and outliers |
| Data Manager | <8% | - Data import and connection - Data transformation and preparation - Data profiling and quality checks |
| Introduction to Predictive Analytics | >12% | - Use cases for predictive algorithms - Basic predictive modeling concepts - Key capabilities of SAP Predictive Analytics |
| Data Science Supporting Automated Analytics | 8%-12% | - Data partition strategies - Model curves and interpretation - Data encoding and transformation |
| Time Series Modeling with Modeler | 8%-12% | - Building and adjusting time series models - Time series concepts - Forecasting and result analysis |
| Social and Recommendation Modeling | <8% | - Recommendation algorithms - Social network analysis basics |
| Predictive Factory | >12% | - Deviation analysis and monitoring - Key features and architecture - Creating and scheduling tasks |
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