Adobe AD0-E600 Dumps Questions Answers

Model scoring in the Data Science Workspace in Adobe Experience Platform is the process of applying a model to a data set. Scoring in Adobe Experience Platform Data Science Workspace can be achieved by feeding input data into an existing trained model. Scoring results are then stored and viewable in a specified output dataset as a new batch.

If a previously defined mandatory field is no longer available due to some changes in the inbound data, the data architect responsible for maintaining existing schemas has the following options:

• Remove the previously defined field: This option is not appropriate because the field is mandatory and removing it would cause issues with data ingestion.
• Make the current mandatory field optional: This option is appropriate because the field is no longer available and making it optional would allow data ingestion to continue without issues.
• Rename the existing field: This option is not appropriate because renaming the field would cause issues with data ingestion.
• Assign the field a new data type: This option is not appropriate because the field is mandatory and changing its data type would cause issues with data ingestion.

Therefore, the data architect can make the current mandatory field optional.

A training run involves generating insights from your data while a scoring run is about learning patterns from your data. A training run requires that a scoring run has occurred. References:https://experienceleague.adobe.com/docs/experience-platform/data-science-workspace/overview.html?lang=en#data-science-training-and-scoring