Identify Data Model Attributes SPLK-1002 Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A Splunk admin is designing a data model for a security operations team. The team wants to ensure that every event in the 'Authentication' dataset has a 'user' field, because their searches always filter on this field. However, they also want to allow events that do not have a 'source' field, as some authentication sources do not provide it. How should the admin define the 'user' and 'source' attributes?

   • ASet both as optional
   • BSet 'user' as required and 'source' as optional
   • CSet 'user' as optional and 'source' as required
   • DSet both as required
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2. 2application · medium

   A Splunk admin is designing a data model for a network monitoring system. The 'bytes_in' field is sometimes missing from events. The admin wants to run a search that calculates the average bytes_in for all events, but only for events that have the field. The admin wants to ensure that the data model does not exclude events that lack the field. What attribute type should be used for 'bytes_in'?

   • ANormal
   • BMultivalued
   • COptional
   • DRequired
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4. 3foundation · easy

   What does it mean when an attribute is marked as 'required' in a data model?

   • AThe attribute is always present in the dataset definition, even if it is not used in a search.
   • BThe attribute is automatically added to all reports created from the data model.
   • CThe attribute must be used in every search that references the data model.
   • DThe attribute must have a non-null value in every event.
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5. 4foundation · easy

   Which of the following is a property of an attribute in a Splunk data model?

   • AThe index from which the attribute is extracted.
   • BThe visualization type used in reports.
   • CThe field name and data type.
   • DThe time range of the data model.
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6. 5foundation · easy

   Why is attribute inheritance useful in a Splunk data model?

   • AIt enforces data quality by requiring all fields to be populated.
   • BIt speeds up searches by pre-computing field values.
   • CIt allows child datasets to automatically have the same fields as the parent, avoiding duplication.
   • DIt reduces the amount of data indexed by the data model.
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