Explanation: In Splunk, a variable for a macro is defined by placing the variable name inside dollar signs, like this: $variable name$. This syntax allows the macro to dynamically replace the variable with the appropriate value when the macro is invoked within a search. Using this method ensures that the search strings can be dynamically adjusted based on the variable's value at runtime.

Explanation: 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 ina search string2. Therefore, option E is correct, while options A, B, C and D are incorrect because they are valid comparison operators in Splunk

Explanation: The searches A and C show a valid use of a macro. A macro is a reusable piece of SPL code that can be called by using single quotes (‘’). A macro can take arguments, which are passed inside parentheses after the macro name. For example, ‘makeMyField(oldField)’ calls a macro named makeMyField with an argument oldField. The searches B and D are not valid because they use double quotes (“”) instead of single quotes (‘’).

Explanation:
In Splunk, the order of operations for Boolean logic in the where command follows this sequence:
Parentheses: Operations inside parentheses are evaluated first.
NOT: The NOT operator is evaluated after parentheses.
AND: The AND operator is evaluated next.
OR: Finally, the OR operator is evaluated last.
This order ensures that expressions within parentheses are given priority, followed by negations (NOT), conjunctions (AND), and finally disjunctions (OR).

Explanation:
The following commands are used when creating visualizations: geom, geostats, and iplocation. Visualizations are graphical representations of data that show trends, patterns, or comparisons. Visualizations can have different types, such as charts, tables, maps, etc. Visualizations can be created by using various commands that transform the data into a suitable format for the visualization type. Some of the commands that are used when creating visualizations are:
geom: This command is used to create choropleth maps that show geographic regions with different colors based on some metric. The geom command takes a KMZ file as an argument that defines the geographic regions and their boundaries. The geom command also takes a field name as an argument that specifies the metric to use for coloring the regions.
geostats: This command is used to create cluster maps that show groups of events with different sizes and colors based on some metric. The geostats command takes a latitude and longitude field as arguments that specify the location of the events. The geostats command also takes a statistical function as an argument that specifies the metric to use for sizing and coloring the clusters.
iplocation: This command is used to create location-based visualizations that show events with different attributes based on their IP addresses. The iplocation command takes an IP address field as an argument and adds some additional fields to the events, such as Country, City, Latitude, Longitude, etc. The iplocation command can be used with other commands such as geom or geostats to create maps based on IP addresses.

Explanation: The correct answer is C. Creates reports without using SPL. This is because the Pivot tool is a feature of Splunk that allows you to report on a specific data set without using the Splunk Search Processing Language (SPL). You can use a drag-and-drop interface to design and generate pivots that present different aspects of your data in the form of tables, charts, and other visualizations. You can learn more about the Pivot tool from the Splunk documentation1or watch a video tutorial2. The other options are incorrect because they do not describe the features of the Pivot tool. The Pivot tool requires data models and datasets to define the data that you want to work with. Data models and datasets are designed by the knowledge managers in your organization. You can learn more about data models and datasets from the Splunk documentation3. The Pivot tool does not create lookups, which are tables that match field values to other field values. You can create lookups using SPL or the Lookup Editor. You can learn more about lookups from the Splunk documentation.

Explanation:
The correct syntax to find events associated with a tag in Splunk istag=1. So, the correct answer isD. tag=.This syntax allows you to annotate specified fields in your search results with tags1.
In Splunk, tags are a type of knowledge object that you can use to add meaningful aliases to field values in your data1. For example, if you have a field calledstatus_codein your data, you might have different status codes like 200, 404, 500, etc. You can create tags for these status codes likesuccessfor 200,not_foundfor 404, andserver_errorfor 500.Then, you can use thetagcommand in your searches to find events associated with these tags1.
Here is an example of how you can use thetagcommand in a search:
index=main sourcetype=access_combined | tag status_code
In this search, thetagcommand annotates thestatus_codefield in the search results with the corresponding tags.If you have tagged the status code 200 withsuccess, the status code404 withnot_found, and the status code 500 withserver_error, the search results will include these tags1.
You can also use thetagcommand with a specific tag value to find events associated with that tag. For example, the following search finds all events where the status code is tagged withsuccess:
index=main sourcetype=access_combined | tag status_code | search tag::status_code=success
In this search, thetagcommand annotates thestatus_codefield with the corresponding tags, and thesearchcommand filters the results to include only events where thestatus_codefield is tagged withsuccess1.

Explanation:
The correct answer is D. field2 values are replaced with the value of the field1.
A field alias is a way to associate an additional (new) name with an existing field name. A field alias can be used to normalize fields from different sources that have different names but represent the same data. Field aliases can also be used to rename fields for clarity or convenience1.
When you create a field alias in Splunk Web, you can select the Overwrite Field Values option to change the behavior of the field alias. This option affects how the Splunk software handles situations where the original field has no value or does not exist, as well as situations where the alias field already exists as a field in your events, alongside the original field2.
If you select the Overwrite Field Values option, the following rules apply:
If the original field does not exist or has no value in an event, the alias field is removed from that event.
If the original field and the alias field both exist in an event, the value of the alias field is replaced with the value of the original field.
If you do not select the Overwrite Field Values option, the following rules apply:
If the original field does not exist or has no value in an event, the alias field is unchanged in that event.
If the original field and the alias field both exist in an event, both fields are retained with their respective values.
Therefore, if you create a field alias where field1—field2 and select the Overwrite Field Values option, and an event only contains values for field1, then the value of field2 will be replaced with the value of field1.