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The C-PAII10-35 exam consists of 80 multiple-choice questions, which must be completed in 180 minutes. The test is available in multiple languages and can be taken both online and in-person. It is advisable for candidates to have a good understanding of SQL, statistics, machine learning algorithms, and modeling techniques.
SAP Certified Application Associate - SAP Predictive Analytics (C-PAII10-35) certification exam is designed for individuals who want to showcase their knowledge and understanding of SAP Predictive Analytics. It is a widely recognized certification that demonstrates the candidate's ability to employ various tools and techniques to extract insights from data for informed decision-making. C_PAII10_35 exam covers a variety of topics, including data mining, predictive modeling, and scoring, which are crucial skills for professionals involved in data analysis.
Reference: https://training.sap.com/certification/c_paii10_35-sap-certified-application-associate--sap-predictive-analytics-g/
To be eligible for the SAP C-PAII10-35 certification exam, you must have experience in using SAP Predictive Analytics. This can be gained through training courses, on-the-job experience, or a combination of both. SAP Certified Application Associate - SAP Predictive Analytics certification exam is intended for application associates who are responsible for implementing and supporting SAP Predictive Analytics solutions.
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The C-PAII10-35 certification exam consists of 80 multiple-choice questions, which must be completed within three hours. C_PAII10_35 exam is available in several languages, including English, German, Japanese, and Chinese. Candidates can take the exam at any authorized SAP testing center, and the exam fee is $500.
SAP C_PAII10_35 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Regression Modeling with Modeler | <8% | - Regression model development
- Validation and application of regression models
|
| Topic 2: Social and Recommendation | <8% | - Recommendation model usage
- Social data analysis concepts
|
| Topic 3: Predictive Factory | 12% | - Predictive Factory architecture and components
- Model deployment and management
|
| Topic 4: Time Series with Modeler | 8% - 12% | - Forecast analysis
- Time series model creation
|
| Topic 5: Data Manager | <8% | - Scheduling and task management
- Data manipulation and dataset creation
|
| Topic 6: Clustering with Automated Analytics | 8% - 12% | - Clustering techniques and application
- Analyzing cluster results
|
| Topic 7: Classification Modeling with Modeler | 12% | - Model evaluation and application
- Classification model building
|
| Topic 8: Data Science Supporting Automated Analytics | 8% - 12% | - Role of data science in predictive analytics
- Data pre-processing
|
| Topic 9: Basics of Automated Analytics | <8% | - Operational workflows
- Automated analytics core capabilities
|
| Topic 10: Introduction to Predictive Analytics | 12% | - Key features of SAP Predictive Analytics
- Fundamental predictive analytics concepts
|