How can you refine your model in the Select Variables tool in Automated Analytics? Note: There are 2 correct answers to this question.
A. Select the model iteration.
B. Analyze variable deviations.
C. Simulate the model application.
D. Display variable correlations.
The operation of the Automated Analytics can be subdivided into four phases: Note: There are 4 correct answers to this question.
A. Data access
B. Data manipulation and preparation
C. Data modeling
D. the Automated Analytics
E. Model presentation and deployment
The selection of the degree of the polynomial depends on the nature of the data to be analyzed. The recommended method is to:
Note: There are 2 correct answers to this question.
A. First generate a model with a first order model. In the large majority of cases, this degree will be sufficient to guarantee a relevant and robust model.
B. The system account of the authenticated user, this is the default with the system authentication.
C. A different system account, as specified in a "User-Mapping" file. This file will specify, for each authenticated user name the name of a system account to be used. This feature is available only on Linux.
D. Test the results thus obtained with models of greater degree, if the performance of the first order model seems inadequate
To help you validate the description when using the Analyze option, you can display the first hundred lines of your dataset.
Note: There are 4 correct answers to this question.
A. Click the button View Data. A new window opens displaying the dataset top lines.
B. In the field First Row Index, enter the number of the first row you want to display.
C. In the field Last Row Index, enter the number of the last row you want to display.
D. Click the Refresh button to see the selected rows.
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
The Model Autosave panel allows you to activate the option that will automatically save the model at the end of the generation process and to set the parameters needed when saving the model. To activate the option, proceed as follows:
Note: There are 3 correct answers to this question.
A. In the Summary of Modeling Parameters panel, click the Autosave button. The Model Autosave panel is displayed.
B. Check Enable Model Autosave.
C. Be granted the SELECT SQL Privileges.
D. Set the parameters listed in the following table.
You work for an automobile manufacturer and wish to send a promotional mailing to your prospects.
Modeler Regression/Classification allows you to:
Note: There are 2 correct answers to this question.
A. Understand why previous prospects responded to such a mailing
B. The user who runs SAP Predictive Analytics is also the one used to install the software
C. Predict the response rate to such a mailing sent to new prospects.
In the Random Forest algorithm, each regressor and classifier is built on a bootstrapped sample. At each split, a random sample of m features is considered for splitting from a total of M features. What is the default setting for m?
Note: There are 1 correct answers to this question.
A. m = sqrt(M) for regression and M/3 for classification
B. m = M/3 for regression and M/3 for classification
C. m = sqrt(M) for regression and sqrt (M) for classification
D. m = M/3 for regression and sqrt (m) for classification
Automated Analytics supports the following data sources:
Note: There are 2 correct answers to this question.
A. Text files supports CR + LF
B. The server software requires about 700 MB for the server and about 200 MB for each client installed on separate machines. No additional storage is required for data because the application does not create a separate data store.
C. Database management systems that can be accessed using ODBC. Note For the list of supported ODBC-compatible sources, see the SAP Product Availability Matrix http://service.sap.com/sap/support/ pam. For more information about using SAP HANA, see the related information below. To configure Automated Analytics modeling tools to access data in your database management system, refer to the guide Connecting your Database Management System on Windows or Connecting your Database Management System on Linux.
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 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 shsemantic, which first defines the variable, then exports it. To guarantee a proper functioning,
Once the models have been generated, model performance indicators, plots and modeling reports in HTML format facilitate viewing and interpretation of the data modeling results. Once the models have been validated, you can apply them to :
Note: There are 2 correct answers to this question.
A. One or more specific observations taken from your database
B. A new, complete dataset or application dataset
C. System authentication is to be used through Pluggable Authentication Module (PAM). Access to Linux System account password required root privileges.
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