SAP Certified Application Associate - SAP Predictive Analytics - C_PAII10_35 Free Exam Questions

A. Click the Refresh button to see the selected rows.

B. In the field Last Row Index, enter the number of the last row you want to display.

C. In the field First Row Index, enter the number of the first row you want to display.

D. Click the button View Data. A new window opens displaying the dataset top lines.

E. Set this group as the group owner of the tmp directory. This should be done if the application has been set up with a user account that has this group set as its primary group

A. The existing R is uninstalled and the registry entries and the R installation folder are removed from the machine

B. In the field for good/bad odds ratio of, enter the ratio.

C. In the field Risk Score, enter the score you want to associate with a good/bad odds ratio.

D. Click the button View Score Table to display the table of scores associated with the corresponding good/bad odds ratio.

E. Indicate the increase of score points needed to double the odds in the field Points to double odds.

A. Improving the prediction confidence of the model, or

B. System authentication is to be used through Pluggable
Authentication Module (PAM). Access to Linux System
account password required root privileges.

C. Improving the predictive power of the model, or

D. Improving both the predictive power and he prediction
confidence of the model.

A. The client application must be able to access both the name
server and the SAP Predictive Analytics server.

B. The SAP Predictive Analytics server must be able to access
the name server.

C. Descriptive models, which allow one to describe datasets.

D. Predictive and explanatory models, which allow one to predict
and explain phenomena,

A. the apply-in dataset (able, view, select statement, data manipulation) and the results dataset are tables coming from the same database,

B. granted access to read and write (create table)

C. the in-database apply mode is not deactivated,

D. A web server such as Apache Web Server or Windows Internet Information Services (IIS).

E. no error has occurred,

F. the model has been computed while at least one physical key variable was defined in the application,

A. the user who runs SAP Predictive Analytics is also the one used to
install the software

B. Understand why previous prospects responded to such a mailing,

C. Predict the response rate to such a mailing sent to new prospects.

A. With standalone installers on each targeted client system

B. a higher bound ([ ]) that can be either open or closed.

C. a minimum value (Minimum)

D. a lower bound ([ ]) that can be either open or closed,

E. a maximum value (Minimum)

A. 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.

B. 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,

C. 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.