Online Access Free C_PAII10_35 Practice Test

To Validate the Model Generated :
Note: There are 2 correct answers to this question.

• A.Verify the Predictive Power Automated Analytics User Guides and Scenarios Modeler If the performance of the model meets your requirements, go to Step 3 - Analyzing and Understanding the Model Generated. Otherwise, go to the procedure To Generate a New Model.
• B.A web server such as Apache Web Server or Windows Internet Information Services (IIS).
• C.You can also check other indicators provided in addition to KI and KR during the model generation. For example, you could view the total elapsed time required to generate the model and information on the standard error rate.

The panel Deviation Analysis Debriefing allows you to follow the analysis process thanks to a progression bar. At the end of the process, a debriefing panel is displayed. For details on the debriefing panel, see the topic on Understanding the Deviation Analysis. You can use the toolbar provided on the upper part of the panel to:
Note: There are 3 correct answers to this question.

• A.stop the analysis process, by clicking the button,
• B.On a test environment, the software may be installed as a standard application.
• C.display the text log detailing the process, by clicking the button,
• D.copy, print or save the debriefing panel .

If you let the structure as undefined, the application uses consistent
coder to automatically determine the categories grouping depending on their interaction with the target variable. You can configure two parameters in this case:
Note: There are 2 correct answers to this question.

• A.Modeler - Data Encoding optimal grouping for all variables
• B.listing active connections, with information such as user, last activity time stamp, number of current models,
• C.The band count for continuous variables
• D.listing the state of models being within each connection,

Two indicators specific to Automated Analytics allow you
to evaluate the performance of a model.
Note: There are 2 correct answers to this question.

• A.The predictive power, which is the quality indicator of the model, Automated Analytics User Guides and Scenarios Modeling Concepts P U B L I C 27
• B.Through Java Web Start: in this mode, a Web server needs to be installed and configured on the machine hosting the SAP Predictive Analytics server. It will deliver, through web pages, the necessary content in order for the client computer to run the app
• C.The prediction confidence, which is the robustness indicator.

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.

• 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.