SAP C_PAII10_35 certification exam is structured as a multiple-choice test consisting of 80 items. Candidates are given a total of 180 minutes to complete the exam, and the passing score is 63%. C-PAII10-35 exam is administered at Pearson Vue testing centers worldwide, and candidates can register online through the SAP website or the Pearson Vue website.
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SAP C_PAII10_35 certification exam is designed to validate the skills and knowledge of professionals who want to demonstrate their expertise in SAP Predictive Analytics. SAP Certified Application Associate - SAP Predictive Analytics certification exam is intended to test the candidates' ability to use SAP Predictive Analytics to perform data analysis and predictive modeling tasks. C-PAII10-35 exam measures the skills required to work with SAP Predictive Analytics, including data preparation, modeling, and deployment.
SAP C-PAII10-35 exam evaluates candidates on their knowledge and skills related to predictive analysis, statistical modeling, and machine learning. It assesses whether or not they are able to apply these concepts using SAP Predictive Analytics software. Through the exam, candidates can demonstrate their proficiency in developing predictive models, creating data sets, and generating predictions using various techniques such as regression analysis.
Reference: https://training.sap.com/certification/c_paii10_35-sap-certified-application-associate--sap-predictive-analytics-g/
SAP C-PAII10-35 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Regression Modeling with Modeler | 8%-12% | - Interpreting regression outputs
- Model validation and use cases
- Building regression models
|
| Basics of Automated Analytics | <8% | - Data storage and preparation
- Data types and variable roles
- Handling missing values and outliers
|
| Clustering with Automated Analytics | 8%-12% | - Applying clustering results
- Building clustering models
- Model debriefing and validation
|
| Predictive Factory | >12% | - Key features and architecture
- Creating and scheduling tasks
- Deviation analysis and monitoring
|
| Time Series Modeling with Modeler | 8%-12% | - Building and adjusting time series models
- Forecasting and result analysis
- Time series concepts
|
| Social and Recommendation Modeling | <8% | - Social network analysis basics
- Recommendation algorithms
|
| Introduction to Predictive Analytics | >12% | - Key capabilities of SAP Predictive Analytics
- Use cases for predictive algorithms
- Basic predictive modeling concepts
|
| Data Science Supporting Automated Analytics | 8%-12% | - Data partition strategies
- Data encoding and transformation
- Model curves and interpretation
|
| Classification Modeling with Modeler | 8%-12% | - Model performance evaluation
- Building classification models
- Deployment and application
|
| Data Manager | <8% | - Data profiling and quality checks
- Data import and connection
- Data transformation and preparation
|