Data models and pivot are both knowledge objects in Splunk that allow you to analyze and visualize your data
in different ways. Data models are collections of datasets that represent your data in a structured and
hierarchical way. Data models define how your data is organized into objects and fields. Pivot is a user
interface that allows you to create data visualizations that present different aspects of a data model. Pivot does
not require users to input SPL searches on data models, but rather lets them select options from menus and
forms. Data models are not created out of datasets called pivots, but rather pivots are created from datasets in
data models.

A comparison operator is a symbol that compares two values and returns a Boolean result (true or false)2. Splunk supports various comparison operators such as <, >, =, !=, <=, >=, IN and LIKE2. However,
?= is not a valid comparison operator in Splunk and will cause a syntax error if used in a search string2.
Therefore, option E is correct, while options A, B, C and D are incorrect because they are valid comparison operators in Splunk

The fillnull command can be used to replace null values in specific fields. The correct equivalent expression for the given eval statement would involve using fillnull twice, once for field1 to replace null values with 0, and once for field2 to replace null values with "NO-VALUE".
Reference:
Splunk Docs - fillnull command

As mentioned before, there are two types of workflow actions: GET and POST1. Both types of workflow
actions can be executed from search results by clicking on an event field value that has a workflow action
configured for it1. Another type of workflow action is Search, which runs another search based on the field
value1. Therefore, options A, B and D are correct, while option C is incorrect because LOOKUP is not a type
of workflow action.

Explanation/Reference: https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/Definesearchmacros