C_PAII10_35 Exam Questions and Answers

QUESTION: 1

Once the models have been generated, model performance indicators, plots and modeling reports in HTML format facilitate viewing and interpretation of the data modeling results. Once the models have been validated, you can apply them to :

Note: There are 2 correct answers to this question.

One or more specific observations taken from your database
A new, complete dataset or application dataset
System authentication is to be used through Pluggable Authentication Module (PAM). Access to Linux System account password required root privileges.

Answer(s): A,B

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QUESTION: 2

Once the model has been generated, you must verify its validity by examining the performance indicators:

Note: There are 2 correct answers to this question.

The predictive power allows you to evaluate the explanatory power of the model, that is, its capacity to explain the target variable when applied to the training dataset. A perfect model possesses a predictive power of 1 and a completely random model possesses a predictive power of 0. No minimum threshold is required for the predictive power of a model. This depends upon the context of your work, that is, your domain of application, the nature of your data and your business issue. 26 P U B L I C Automated Analytics User Guides and Scenarios Modeling Concepts In some cases, a model with a predictive power as low as 0.1 may allow realization of a profit of several thousands dollars. In all cases, a positive predictive power indicates that the model generated will perform better than a random model.
The prediction confidence defines the degree of robustness of the model, that is, its capacity to achieve the same explanatory power when applied to a new dataset. In other words, the degree of robustness corresponds to the predictive power of the model when applied to an application dataset. A model with a prediction confidence inferior to 0.95 must be considered with caution. The performance of such a model is very likely to vary between the training dataset and the application datasets.
The environment variable definition is using the POSIX standard shell sh semantic, which first defines the variable, then exports it. To guarantee a proper functioning, The environment variable definition is using the POSIX standard shell sh
semantic, which first defines the variable, then exports it. To guarantee a proper functioning.

Answer(s): A,B

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QUESTION: 3

Automated Analytics supports the following data sources:

Note: There are 2 correct answers to this question.

Text files supports CR + LF
The server software requires about 700 MB for the server and about 200 MB for each client installed on separate machines. No additional storage is required for data because the application does not create a separate data store.
Database management systems that can be accessed using ODB Note For the list of supported ODBC-compatible sources, see the SAP Product Availability Matrix http://service.sap.com/sap/support/pam. For more information about using SAP HANA, see the related information below. To configure Automated Analytics modeling tools to access data in your database management system, refer to the guide Connecting your Database Management System on Windows or Connecting your Database Management System on Linux.

Answer(s): A,C

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QUESTION: 4

You work for an automobile manufacturer and wish to send a promotional mailing to your prospects. Modeler Regression/Classification allows you to:

Note: There are 2 correct answers to this question.

Understand why previous prospects responded to such a mailing.
the user who runs SAP Predictive Analytics is also the one used to install the software
Predict the response rate to such a mailing sent to new prospects.

Answer(s): A,C

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QUESTION: 5

In the Random Forest algorithm, each regressor and classifier is built on a bootstrapped sample. At each split, a random sample of m features is considered for splitting from a total of M features. What is the default setting for m?

Note: There are 1 correct answers to this question.

m = sqrt(M) for regression and M/3 for classification
m = M/3 for regression and M/3 for classification
m = sqrt(M) for regression and sqrt (M) for classification
m = M/3 for regression and sqrt (m) for classification

Answer(s): D

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SAP C_PAII10_35 Exam (page: 1)
SAP Certified Application Associate - Predictive Analytics
Updated on: 12-Feb-2026

Access free C_PAII10_35 actual exam questions, answers, explanations and user discussions.

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SAP C_PAII10_35 Exam (page: 1) SAP Certified Application Associate - Predictive Analytics Updated on: 12-Feb-2026

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SAP C_PAII10_35 Exam (page: 1)
SAP Certified Application Associate - Predictive Analytics
Updated on: 12-Feb-2026