Free SAP C-PAII10-35 Exam Dumps Questions & Answers

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 prediction confidence, which is the robustness indicator.
• B.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
• C.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

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 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.
• 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 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.

In data mining, there are two types of models:
Note: There are 2 correct answers to this question.

• A.The SAP Predictive Analytics server must be able to access
  the name server.
• B.The client application must be able to access both the name
  server and the SAP Predictive Analytics server.
• C.Descriptive models, which allow one to describe datasets.
• D.Predictive and explanatory models, which allow one to predict
  and explain phenomena,

A polynomial may be of degree 1, 2, 3 or greater. By
defining the polynomial degree, you are defining the degree of
complexity of the model. Examples of Polynomials:
Note: There are 2 correct answers to this question.

• A.A polynomial of degree 1 is of the form: f
• B.The server certificate
• C.The server private key
• D.Xn A polynomial of degree 2 is of the form: f

Depending on the type of the target, the model graph plot allows you to:
Note: There are 3 correct answers to this question.

• A.A web server such as Apache Web Server or Windows Internet Information Services (IIS).
• B.View the realizable profit that pertains to your business issue using the model generated when the target is nominal.
• C.Compare the predicted value to the actual value when the target is continuous .
• D.Compare the performance of the model generated with that of a random type model and that of a hypothetical perfect model when the target is nominal.