최신SAP Certified Application Associate - SAP Predictive Analytics - C-PAII10-35무료샘플문제

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 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.

C. 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,

A. a neural network,

B. a Certification Authority certificate (CA certificate)

C. a mathematical function.

D. a decision tree,

A. Resources are used more fully because each modeling session has a dedicated process. The process size limit applies only to a single user

B. Define the role of variables contained in the dataset to be analyzed. You may select one or more variables as target variables. These are the variables that corresponds with your business issue. The other variables of the table of data are considered to be explanatory variables: they allow calculation of the value of the target variable in a given context. They may also be used as weight variables.

C. Describe the data. A utility integrated with the application allows you to generate a description of the dataset to be analyzed, automatically. You need only validate that description, verifying that the type and storage format of each variable were identified correctly.

D. Users are authenticated because clients must log in before being able to use the modeling server. User accounts can be configured to implement security policy.

A. "0.79" is capable of explaining 79% of the information contained in the target variable using the explanatory variables contained in the dataset analyzed.

B. The system account of the authenticated user, this is the default with the system authentication

C. "0" is a purely random model

D. "1" is a hypothetical perfect model, capable of explaining 100% of the target variable using the explanatory variables contained in the dataset analyzed. In practice, such a predictive power would generally indicate that an explanatory variable 100% correlated with the target variable was notexcluded from the dataset analyzed.

A. Two tabs located in the left bottom part of the panel provide you with information on the nodes and with the profit curve corresponding to the current decision tree

B. The decision tree itself, which is displayed in the upper section of the panel

C. A navigator allowing you to visualize what part of the tree you are studying is displayed in the right bottom part of the panel.\

D. A consequence of 1 is that this metric is resistant to outliers: when there are a few occurrences of the target with very high values with respect to the rest of the target value distributions, the notion of normal profit is not impacted.

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

A. The server port

B. The signal column

C. The date column,

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

B. One or more specific observations taken from your database

C. A new, complete dataset or application dataset