C_PAII10_35 Online Practice Questions and Answers

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

The application allows you to refine a currently open model. For instance, you can: Note: There are 2 correct answers to this question.

A. Reduce the number of explanatory variables used by the model while maintaining the initial quality

B. Generate a model of degree 2 using the most significant variables of the degree 1 model.

C. The server port (by default: 12347 for the authenticated server)

Once your business issue has been identified and formulated, you need to have data on hand that will permit an answer to be found. In order for your data to be usable by the application, the following five conditions must be met:

Note: There are 5 correct answers to this question.

A. You must have a sufficiently large volume of data to be able to build a valid model, that is, in order for the model to be both relevant and robust. An analytical model that is generated from a dataset of 50 lines may have low generalization capacity, and contain low informative value. We can advise you on the issues of data volume.

B. Your dataset must contain a target variable, that will allow you to express your business issue within the application.

C. The target variable must be known for each observation of the training dataset. To express this another way, no target variable values may be missing over the range of the entire training dataset.

D. The data source format must be supported by the application.

E. Your data must be presented in the form of a single table of data , except in instances where you are using Event Logging or Sequence Coding features of Data Manager.

F. Server instances are going to be started with a different system account than the authenticated server.

To Start SAP Predictive Analytics:

Note: There are 2 correct answers to this question.

A. Select Start Programs SAP Business Intelligence SAP

B. Predictive Analytics Desktop SAP Predictive Analytics . The SAP Predictive Analytics start panel appears.

C. Click the feature you want to use.

D. Allow the group to write there. This is automatically done when using the post installation configuration script.

You developed an unsupervised segmentation model in Automated Analytics. What should you analyze when debriefing the model?

Note: There are 1 correct answers to this question.

A. The cluster target means

B. The overall model Ki

C. The cluster frequencies

D. The winning number of clustersc

Detected profit is the default setting for type of profit. Using this type of profit: Note: There are 2 correct answers to this question.

A. The value "0" is assigned to observations that do not belong to the target category of the target variable,

B. The value "1/(frequency of the target variable in the dataset) is assigned to observations that do belong to the target category of the target variable.

C. Modeler enables the analyst to create in a homogenous and easy-handling workflow models such as classification, regression, clustering, time series, and association rules. Models can be exported in different formats so that you can easily apply them in your production environment.

D. Data Manager is used to facilitate the preparation of the data to be used in the analytics project.

Depending on the type of the target, the model graph plot allows you to:

Note: There are 3 correct answers to this question.

A. View the realizable profit that pertains to your business issue using the model generated when the target is nominal.

B. 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.

C. A web server such as Apache Web Server or Windows Internet Information Services (IIS).

D. Compare the predicted value to the actual value when the target is continuous .

Once the model has been generated, you must verify its validity by examining the performance indicators: Note: There are 4 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 would possess a predictive power equal to 1 and a completely random model would possess a predictive power equal to 0

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 applied to an application dataset. For this scenario, the model generated has the following performance indicators:

C. A quality indicator KI equal to 0.808

D. A robustness indicator KR equal to 0.992

E. The existing R is uninstalled and the registry entries and the R installation folder are removed from the machine

Data modeling with time series is subdivided into four broadly defined stages: Note: There are 4 correct answers to this question.

A. Defining the Modeling Parameters

B. Generating and Validating the Model

C. Analyzing and Understanding the Analytical Results

D. Using a Generated Mode E. The Name Server

Understanding the Decision Tree Panel The panel Decision Tree is split into three parts: Note: There are 3 correct answers to this question.

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

B. 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

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.