C_PAII10_35 試験問題を無料オンラインアクセス

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

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

A user has created several analyses with a chain in the
Predict room. What data can the user leverage in a
visualization in the Visualize room when these analyses
are executed successfully?
Note: There are 1 correct answers to this question.

• A.Data from all components from one analysis
• B.Data from all components from all analyses
• C.Data from one component from all analyses
• D.Data from one component from one analysis

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

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

How are missing values handled in Automated
Analytics?
Note: There are 1 correct answers to this question.

• A.They are automatically ignored.
• B.They are automatically filled.
• C.They are considered as outliers.
• D.They are assigned to a category.

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 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,
• B.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.
• 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.