C_PAII10_35 - SAP Certified Application Associate – SAP Predictive Analytics Practice Exam

C_PAII10_35 - SAP Certified Application Associate – SAP Predictive Analytics Practice Exam

The C_PAII10_35 certification validates your grasp of the fundamental concepts and functionalities of SAP Predictive Analytics. This credential demonstrates your ability to participate in SAP Predictive Analytics projects as part of a team under the guidance of experienced professionals.

Who Should Take This Exam?

• Entry-level data analysts or business analysts: Individuals seeking to launch their careers in the field of predictive analytics, specifically using SAP solutions.
• SAP consultants (beginners): New consultants aiming to build a foundation for working on SAP Predictive Analytics implementations.
• Business users: Those who will be utilizing SAP Predictive Analytics reports and insights generated from data analysis.

Prerequisites

There are no formal prerequisites for taking the exam. However, a basic understanding of data analysis concepts, business processes, and familiarity with SAP terminology would be beneficial.

Roles and Responsibilities 

• SAP Predictive Analytics Consultants: Implementing, configuring, and managing SAP Predictive Analytics for data exploration, modeling, and generating insights.
• Data Analysts: Utilizing SAP Predictive Analytics tools to analyze data, build predictive models, and communicate findings to stakeholders.
• Business Intelligence (BI) Professionals: Integrating SAP Predictive Analytics with other BI tools for comprehensive data analysis and reporting.
• Data Scientists (Junior Level): Contributing to data science projects that leverage SAP Predictive Analytics for data exploration and model building (may require further education).

Exam Details

• Exam Duration 180 mins
• Exam Format Multiple Choice
• Number of Questions 80 Questions

Course Structure

1. 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.

2. 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.

3. Classification Modeling with Modeler > 12%

• Build and apply classification models in Modeler, and implement deviation analysis.

4. Time Series with Modeler 8% - 12%

• Build, debrief and apply a time-series model in Modeler.

5. Clustering with Automated Analytics 8% - 12%

• Build, debrief and apply a clustering model in Modeler.

6. Data Science supporting Automated Analytics 8% - 12%

• Describe data partition strategies, data encoding, and interpretation of model curves.

7. Data Manager < 8%

• Outline how to manipulate data in the Data Manager and how to use it to create dynamic data sets.

8. Basics of Automated Analytics < 8%

• Identify different data types, storage and variable roles, as well as how to handle missing values and outliers.

9. Social and Recommendation < 8%

• Build a social recommendation and analysis.

10. Regression Modeling with Modeler< 8%

• Build, debrief, save and apply a regression model in Modeler.