An event type is a classification of events based on a search query, which allows for a static set of search criteria. In this case, option A (index=server_48 sourcetype=BETA_881 code=220) represents a simple search without transforming commands (e.g., stats, inputlookup). Event types cannot include transforming commands such as stats or lookup.References:Splunk Documentation - Event Types

The correct answer is C. By enclosing the macro name in backtick characters (`).
A macro is a way to reuse a piece of SPL code in different searches. A macro can take arguments, which are variables that can be replaced by different values when the macro is called. A macro can also contain another macro within it, which is called a nested macro1.
To reference a macro in a search, you need to enclose the macro name in backtick characters (). For example, if you have a macro named my_macro` that takes one argument, you can reference it in a search by using the following syntax:
| my_macro(argument) | ...
This will replace the macro name and argument with the SPL code contained in the macro definition. For example, if the macro definition is:
[my_macro(argument)] search sourcetype=$argument$
And you reference it in a search with:
index=main | my_macro(web) | stats count by host
This will expand the macro and run the following SPL code:
index=main | search sourcetype=web | stats count by host
References:
* Use search macros in searches

Reference: https://docs.splunk.com/Splexicon:Calculatedfield
The eval command is used to create new fields or modify existing fields based on an expression2. The output of an eval expression is a calculated field, which is a field that you create based on the value of another field or fields2. You can use calculated fields to enrich your data with additional information or to transform your data into a more useful format2. Therefore, option B is correct, while options A, C and D are incorrect because they are not names of knowledge objects that represent the output of an eval expression.

In Splunk, a data model's root dataset is the foundational element upon which the rest of the data model is built. The root dataset can be of various types, including search, transaction, or event-based datasets. One of the key features of the root dataset is that it automatically inherits the knowledge objects associated with its base search. These knowledge objects include field extractions, lookups, aliases, and calculated fields that are defined for the base search, ensuring that the root dataset has all necessary contextual information from the outset. This allows users to build upon this dataset with additional child datasets and objects without having to redefine the base search's knowledge objects.