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Message type: E = Error
Message class: RSANA_UMM_API - UMM API Messages
Message number: 249
Message text: A draft predictive scenario should have a model
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RSANA_UMM_API249
- A draft predictive scenario should have a model ?The SAP error message RSANA_UMM_API249 indicates that a draft predictive scenario in SAP Analytics Cloud (SAC) is missing a model. This error typically occurs when you are trying to create or save a predictive scenario without associating it with a valid predictive model.
Cause:
- Missing Model Association: The predictive scenario you are trying to create or save does not have an associated predictive model. In SAC, predictive scenarios require a model to perform predictions based on the data.
- Draft State: The scenario is in a draft state, and it may not have been fully configured or saved correctly.
- Model Not Available: The model you are trying to use may not be available in the system or may have been deleted or not published.
Solution:
Associate a Model: Ensure that you have created a predictive model in SAC. You can do this by navigating to the "Model" section and creating a new model or using an existing one. Once you have a model, associate it with your predictive scenario.
Check Model Status: Verify that the model you want to use is published and available for use. If it is in a draft state, publish it first.
Review Scenario Configuration: Go through the configuration of your predictive scenario to ensure that all required fields are filled out correctly and that the model is properly linked.
Create a New Scenario: If the current scenario is not working, consider creating a new predictive scenario from scratch and ensure that you select the appropriate model during the setup process.
Consult Documentation: Refer to the SAP Analytics Cloud documentation for detailed steps on creating predictive models and scenarios. This can provide insights into any additional requirements or configurations needed.
Related Information:
By following these steps, you should be able to resolve the error and successfully create a predictive scenario with an associated model.
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