If in the PAi Fiori application, a predictive model is shown in status error, more information about the cause may be found in the associated application log. If the PAi configuration is incorrect, which following messages may appear in the application log:
Note: There are 2 correct answers to this question.
A. Error: Invalid Table Message For example: invalid table name: sap.hana.pai.Engine::Entities.GTMP.PredictiveScenario: line 1 col 72 an indication that the PAi application is not installed correctly. The solution is to reinstall the PAi DU.
B. Error: ,QsuFLHQt Privilege For example: LnsucLent privilege: Not authorized This means that the PAi HANA role The solution is to consult your database administrator regarding role assignment.
C. In Predictive Factory, you declare one or more modeling servers. (A modeling server is the machine that hosts the Automated Analytics server that will run the modeling tasks). Predictive Factory communicates with the Automated Analytics server using CORBA. All connections to data and model sources are handled via the modeling server.
D. Network administration is simplified because all network traffic from the client is directed to the server. This means only two TCP ports need to be opened for an Automated Analytics installation.
To Validate the Model Generated :
Note: There are 2 correct answers to this question.
A. Verify the Predictive Power Automated Analytics User Guides and Scenarios Modeler If the performance of the model meets your requirements, go to Step 3 - Analyzing and Understanding the Model Generated. Otherwise, go to the procedure To Generate a New Model.
B. You can also check other indicators provided in addition to KI and KR during the model generation. For example, you could view the total elapsed time required to generate the model and information on the standard error rate.
C. A web server such as Apache Web Server or Windows Internet Information Services (IIS).
There are two ways to define a variable structure:
Note: There are 2 correct answers to this question.
A. by first extracting the categories from the variable statistics, then editing or validating the suggested structure.
B. by importing the structure from an existing model.
C. by building a new structure from scratch.
D. if the user running SAP Predictive Analy
The importance of a category depends on both its difference to the target category mean and the number of represented cases. High importance can result from any of the following:
Note: There are 3 correct answers to this question.
A. A high discrepancy between the category and the mean of the target category of the target variable
B. A minor discrepancy combined with a large number of records in the category
C. A combination of both
D. recise scheduling of main industrialization tasks
The variable "product purchased" is your target variable:
it corresponds to your business issue. It is:
Note: There are 2 correct answers to this question.
A. Known for all values of the training dataset.
B. Not known for the values of the application dataset.
C. knows the main Linux administration commands (managing users, groups, and so on),
D. knows how to set up ODBC drivers on Linux systems.
What is the first phase of the CRISP- DM predictive modeling process? Note: There are 1 correct answers to this question.
A. Model building
B. Business understanding
C. Data understanding
D. Data preparation
There are several interesting things to note about normal profit: Note: There are 3 correct answers to this question.
A. The normal profit of category is independent of the target values themselves will not change
B. A consequence of 1 is that this metric is resistant to outliers: when there are a few
C. 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.
D. To test the latest version of the application without impacting the current production environment.
E. The weighted sum of the normal profit for all categories of a given variables will always be 0.
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.
To describe your data, you can:
Note: There are 2 correct answers to this question.
A. Use an existing description file, that is, taken from your information system or saved from a previous use of Automated Analytics.
B. A web server such as Apache Web Server or Windows Internet Information Services (IIS).
C. Or create a description file using the Analyze option. In this case, it is important that you validate the description file obtained. You can save this file for later re-use.
The Periodic with testing at the end strategy distributes:
Note: There are 4 correct answers to this question.
A. Three lines of the first 4/5 of the initial dataset are distributed to the training subset.
B. One line of the first 4/5 of the initial dataset is distributed to the validation sub-set.
C. If the entire 4/5 of the initial dataset is not yet distributed, distribution operations begin again at step 1. Otherwise, if the entire 4/5 of the initial dataset has been
D. distributed, distribution operations go to step
E. The final 1/5 of the initial dataset is sent as a block of data to the testing sub-set.
F. The name server and the server will be assigned a specific TCP port. By default, the startup scripts we use fix them, and they should probably set properly depending on the network strategy
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