SPLK-1002 Splunk Core Certified Power User Exam Questions and Answers

Data model attributes are fields that are added to a dataset from search time field extractions, calculated fields, lookups, or aliases. They can be created within the data model editor or inherited from a parent dataset. They can be edited or removed unless they are required by the data model. They can be added to any type of dataset, not just root search datasets.ReferencesSee About data models, [Define data model attributes], and [Edit data model datasets] in the Splunk Documentation.

https://docs.splunk.com/Documentation/Splunk/8.0.2/Knowledge/SetupaGETworkflowaction

Define a GET workflow action

Steps

• Navigate to Settings > Fields > Workflow Actions.
• Click New to open up a new workflow action form.
• Define a Label for the action.

The Label field enables you to define the text that is displayed in either the field or event workflow menu. Labels can be static or include the value of relevant fields.

• Determine whether the workflow action applies to specific fields or event types in your data.

Use Apply only to the following fields to identify one or more fields. When you identify fields, the workflow action only appears for events that have those fields, either in their event menu or field menus. If you leave it blank or enter an asterisk the action appears in menus for all fields.

Use Apply only to the following event types to identify one or more event types. If you identify an event type, the workflow action only appears in the event menus for events that belong to the event type.

• For Show action in determine whether you want the action to appear in the Event menu, the Fields menus, or Both.
• Set Action type to link.
• In URI provide a URI for the location of the external resource that you want to send your field values to.

Similar to the Label setting, when you declare the value of a field, you use the name of the field enclosed by dollar signs.

Variables passed in GET actions via URIs are automatically URL encoded during transmission. This means you can include values that have spaces between words or punctuation characters.

• Under Open link in, determine whether the workflow action displays in the current window or if it opens the link in a new window.
• Set the Link method to get.
• Click Save to save your workflow action definition.

https://docs.splunk.com/Documentation/Splunk/8.1.2/SearchReference/Chart

The fields sidebar is a panel that shows the fields that are present in your search results2. The fields sidebar does not show all extracted fields, which are fields that are extracted from your raw data using various methods such as regular expressions, delimiters or key-value pairs2. The fields sidebar only shows selected fields and interesting fields2. Selected fields are fields that you choose to display in your search results by clicking on them in the fields sidebar or by using the fields command2. Interesting fields are fields that appear in at least 20 percent of events or have high variability among values2. Therefore, option C is correct, while options A and B are incorrect because they are types of fields that the fields sidebar does show.

The transaction command allows you to correlate events across multiple sources. The transaction command is a search command that allows you to group events into transactions based on some common characteristics, such as fields, time, or both. A transaction is a group of events that share one or more fields that relate them to each other. A transaction can span across multiple sources or sourcetypes that have different formats or structures of data. The transaction command can help you correlate events across multiple sources by using the common fields as the basis for grouping. The transaction command can also create some additional fields for each transaction, such as duration, eventcount, startime, etc.

The Field Extractor (FX) allows you to use regular expressions (regex) to extract fields from your events using a graphical interface or by manually editing the regex2. When you use the FX to perform a regex field extraction, you can use the require option to specify a string that must be present in an event for it to be included in the extraction2. This way, you can filter out events that do not contain the required string and focus on the events that are relevant for your extraction2. Therefore, option D is correct, while options A, B and C are incorrect.

Field aliases are alternative names for fields in Splunk. Field aliases can be used to normalize data across different sources and sourcetypes that have different field names for the same concept. For example, you can create a field alias for src_ip that maps to clientip, source_address, or any other field name that represents the source IP address in different sourcetypes. Field aliases can also be used in lookup file definitions to map fields in your data to fields in the lookup file. For example, you can use a field alias for src_ip to map it to ip_address in a lookup file that contains geolocation information for IP addresses. Field alias names do not replace the original field name, but rather create a copy of the field with a different name. Field alias names are case sensitive when used as part of a search, meaning that src_ip and SRC_IP are different fields.

A workflow action is a link that appears when you click an event field value in your search results1. A workflow action can open a web page or run another search based on the field value1. There are two types of workflow actions: GET and POST1. A GET workflow action appends the field value to the end of a URI and opens it in a web browser1. A POST workflow action sends the field value as part of an HTTP request to a web server1. You can configure a workflow action to open a web page in either the same window or a new window1. Therefore, option D is correct, while options A, B and C are incorrect.

A space is an implied AND in a search string, which means that it acts as a logical operator that returns events that match both terms on either side of the space2. For example, status=200 method=GET will return events that have both status=200 and method=GET2. Therefore, option B is correct, while options A, C and D are incorrect because they are not implied by a space in a search string.

The transaction command is used to group events into transactions based on some common characteristics, such as fields, time, or both. The transaction command can also specify start and end constraints for the transactions, such as a field value that indicates the beginning or the end of a transaction. The stats command is used to calculate summary statistics on the events, such as count, sum, average, etc. The stats command cannot group events based on start and end constraints, but only on fields or time buckets. Therefore, the transaction command should be used instead of the stats command when you need to group events based on start and end constraints.

The search below groups events by two or more fields (clientip and host), creates transactions with start and end constraints (maxspan=30s and maxpause=5s), and calculates the duration of each transaction.

index=main | transaction clientip host maxspan=30s maxpause=5s

The search does the following:

• It filters the events by the index main, which is a default index in Splunk that contains all data that is not sent to other indexes.
• It uses the transaction command to group events into transactions based on two fields: clientip and host. The transaction command creates new events from groups of events that share the same clientip and host values.
• It specifies the start and end constraints for the transactions using the maxspan and maxpause arguments. The maxspan argument sets the maximum time span between the first and last events in a transaction. The maxpause argument sets the maximum time span between any two consecutive events in a transaction. In this case, the maxspan is 30 seconds and the maxpause is 5 seconds, meaning that any transaction that has a longer time span or pause will be split into multiple transactions.
• It creates some additional fields for each transaction, such as duration, eventcount, startime, etc. The duration field shows the time span between the first and last events in a transaction.

The search string below creates a table of the total count of mysterymeat corndogs split by user.

| stats count by user | where corndog=mysterymeat

The search string does the following:

• It uses the stats command to calculate the count of events for each value of the user field. The stats command creates a table with two columns: user and count.
• It uses the where command to filter the results by the value of the corndog field. The where command only keeps the rows where corndog equals mysterymeat.

Therefore, the search string creates a table of the total count of mysterymeat corndogs split by user.

Search workflow actions are custom actions that run a search when you click on a field value in your search results. Search workflow actions can be configured with various options, such as label name, search string, time range, app context, etc. One of the options is to define the time range of the search when creating the workflow action. You can choose from predefined time ranges, such as Last 24 hours, Last 7 days, etc., or specify a custom time range using relative or absolute time modifiers. Search workflow actions do not run as real-time searches by default, but rather use the same time range as the original search unless specified otherwise. Search workflow actions cannot be configured as scheduled searches, as they are only triggered by user interaction. Search workflow actions can be configured with any valid search string that includes any search command, such as transaction.

https://docs.splunk.com/Documentation/Splunk/8.1.0/SearchReference/ConversionFunctions#tostring.28X.2CY.29

The tostring function in the eval command converts a numeric value to a string value. It can take an optional second argument that specifies the format of the string value. Some of the possible formats are:

• hex: converts the numeric value to a hexadecimal string.
• commas: adds commas to separate thousands in the numeric value.
• duration: converts the numeric value to a human-readable duration string, such as “2h 3m 4s”.

Therefore, the formats A, B, and D can be used with the tostring function.

 The data model command allows you to run searches on data models that have been accelerated1. The syntax for using the data model command is | datamodel [search ]1. Therefore, option A is the correct way to use the data model command to search fields in the data model within the web dataset. Options B and C are incorrect because they do not follow the syntax for the data model command. Option D is incorrect because it does not use the data model command at all.

The eval command supports a number of functions that you can use in your expressions to perform calculations, conversions, string manipulations and more2. One of the eval command functions is tostring(), which converts a numeric value to a string value2. Therefore, option D is correct, while options A, B and C are incorrect because they are not valid eval command functions.

The eval command is used to create new fields or modify existing fields based on an expression2. The sort command is used to sort the results by one or more fields in ascending or descending order2. If you want to convert numeric field values to strings and also sort on those values, you should use the sort command first, then use the eval command to convert the values to strings2. This way, the sort command will use the original numeric values for sorting, rather than the converted string values which may not sort correctly. Therefore, option C is correct, while options A, B and D are incorrect.

GET workflow actions are custom actions that open a URL link when you click on a field value in your search results. GET workflow actions can be configured with various options, such as label name, base URL, URI parameters, app context, etc. One of the options is to choose whether to open the URL link in the current window or in a new window. GET workflow actions do not have to be configured with POST arguments, as they use GET method to send requests to web servers. Configuration of GET workflow actions does not include choosing a sourcetype, as they do not generate any data in Splunk. Label names for GET workflow actions must include a field name surrounded by dollar signs, as this indicates the field value that will be used to replace the variable in the URL link.

The Splunk Common Information Model (CIM) add-on is a collection of pre-built data models and knowledge objects that help you normalize your data from different sources and make it easier to analyze and report on it3. The CIM add-on includes pre-configured data models that cover various domains such as Alerts, Email, Database, Network Traffic, Web and more3. Therefore, option B is correct. The CIM add-on also includes fields and event category tags that define the common attributes and labels for the data models3. Therefore, option C is correct. The CIM add-on does not include custom visualizations or automatic data model acceleration. Therefore, options A and D are incorrect.

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.

The search string below returns events from the data model named Application_State.

| datamodel Application_State All_Application_State search

The search string does the following:

• It uses the datamodel command to access a data model in Splunk. The datamodel command takes two arguments: the name of the data model and the name of the dataset within the data model.
• It specifies the name of the data model as Application_State. This is a predefined data model in Splunk that contains information about web applications.
• It specifies the name of the dataset as All_Application_State. This is a root dataset in the data model that contains all events from all child datasets.
• It uses the search command to filter and transform the events from the dataset. The search command can use any search criteria or command to modify the results.

Therefore, the search string returns events from the data model named Application_State.

POST workflow actions are custom actions that send a POST request to a web server when you click on a field value in your search results. POST workflow actions can be configured with various options, such as label name, base URL, URI parameters, post arguments, app context, etc. One of the options that are required to create a POST workflow action is post arguments. Post arguments are key-value pairs that are sent in the body of the POST request to provide additional information to the web server. Post arguments can include field values from your data by using dollar signs around the field names.

After manually editing a regular expression (regex) that was created using the Field Extractor (FX) UI, it is no longer possible to edit the field extraction in the FX UI. The FX UI is a tool that helps you extract fields from your data using delimiters or regular expressions. The FX UI can generate a regex for you based on your selection of sample values or you can enter your own regex in the FX UI. However, if you edit the regex manually in the props.conf file, the FX UI will not be able to recognize the changes and will not let you edit the field extraction in the FX UI anymore. You will have to use the props.conf file to make any further changes to the field extraction. Changes made manually cannot be reverted in the FX UI, as the FX UI does not keep track of the changes made in the props.conf file. It is possible to manually edit a regex that was created using the FX UI, as long as you do it in the props.conf file.

Therefore, only statement B is true about manually editing a regex.

Auto-Extracted fields in Splunk Data Models are derived directly from the indexed data based on the existing fields within the events. These fields are identified and extracted by Splunk automatically, without the need for explicit field extractions configured by the user. Understanding the characteristics of Auto-Extracted fields is crucial for effectively managing Data Models and utilizing them in Pivot tables for analysis.

A. Auto-Extracted fields can be hidden in Pivot. This is true. When building a Data Model, you have the option to hide certain fields from appearing in Pivot, making the Pivot table cleaner and more focused on the fields that are most relevant for analysis. This helps in reducing clutter and focusing on the data that matters most to the users.

B. Auto-Extracted fields can have their data type changed. This statement is not typically accurate for Auto-Extracted fields. The data type of an Auto-Extracted field is determined by Splunk based on the field's content in the indexed data. While you can assign a type to a field when you manually create a field in a data model, the inherent data type of Auto-Extracted fields is not something that is changed within the Data Model itself.

C. Auto-Extracted fields can be given a friendly name for use in Pivot. This is correct. Within Data Models, you can assign a more user-friendly, descriptive name to an Auto-Extracted field. This feature is particularly useful in making Data Models more intuitive and easier to use for those who may not be familiar with the original field names or when the original field names are not descriptive or user-friendly.

D. Auto-Extracted fields can be added if they already exist in the dataset with constraints. This is true. Auto-Extracted fields are based on fields that already exist in the data. When you define a dataset within a Data Model, you can apply constraints to narrow down the events that the dataset includes. The Auto-Extracted fields are then identified from this constrained dataset. This means that the fields must already be present in the data that meets the dataset's constraints to be available for auto-extraction.

In summary, Auto-Extracted fields in Splunk Data Models offer a flexible and efficient way to utilize existing data fields within Pivot tables, with options to rename them for clarity and hide unnecessary fields to streamline data analysis.

To create a macro that accepts arguments, you must include the number of arguments in parentheses at the end of the macro name1. For example, my_macro(3) is a macro that accepts three arguments. The number of arguments in the macro name must match the number of arguments in the definition1. Therefore, option A is correct, while options B, C and D are incorrect.

This search uses the transaction command to group events that share a common value for JSESSIONID into transactions1. The transaction command assigns a duration field to each transaction, which is the difference between the latest and earliest timestamps of the events in the transaction1. The search then uses the timechart command to create a time-series chart of the average duration of each transaction1. Therefore, option A is correct because it describes the search accurately. Option B is incorrect because the search does not use the stats command or the pause field. Option C is incorrect because the transaction command does not require the startswith and endswith options, although they can be used to specify how to identify the beginning and end of a transaction1. Option D is incorrect because the transaction command does not have to be the last command in the search pipeline, although it is often used near the end of a search1.

The transaction command groups events that share a common value in a specified field, such as JSESSIONID, and that occur within a specified time range. The search command filters the results to show only the events that match the given value of JSESSIONID. This search groups the events by JSESSIONID and then shows only the events that have the value SD462K101C2F267 for JSESSIONID2

1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, transaction command.

The correct answer is C. Distributable streaming. This is because the eval command is a type of command that can run on the indexers before the results are sent to the search head. This reduces the amount of data that needs to be transferred and improves the search performance. Distributable streaming commands can operate on each event or result individually, without depending on other events or results. You can learn more about the types of commands and how they affect search performance from the Splunk documentation1.

https://docs.splunk.com/Documentation/Splunk/8.0.2/Knowledge/CreateworkflowactionsinSplunkWeb

The span option is used to specify the time span for the timechart command. The span value can be a number followed by a time unit, such as h for hour, d for day, w for week, etc. The span value determines how the data is grouped into time buckets. For example, span=12h means that the data is grouped into 12-hour intervals. The timespan option is not a valid option for the timechart command2

1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, timechart command.

Highlighted search terms indicate matching search results in Splunk, which means that they show which parts of your events match your search string2. For example, if you search for error OR fail, Splunk will highlight error or fail in your events to show which events match your search string2. Therefore, option D is correct, while options A, B and C are incorrect because they are not indicated by highlighted search terms.

The tag=Priv* search will return events containing a tag named Privileged, as well as any other tag that starts with Priv. The asterisk (*) is a wildcard character that matches zero or more characters. The other searches will not match the exact tag name.

The correct answer is B. Event category tags. This is because the CIM add-on contains a collection of preconfigured data models that you can apply to your data at search time. Each data model in the CIM consists of a set of field names and tags that define the least common denominator of a domain of interest. Event category tags are used to classify events into high-level categories, such as authentication, network traffic, or web activity. You can use these tags to filter and analyze events based on their category. You can learn more about event category tags from the Splunk documentation12. The other options are incorrect because they are not included with the CIM add-on. Search macros are reusable pieces of search syntax that you can invoke from other searches. They are not specific to the CIM add-on, although some Splunk apps may provide their own search macros. Workflow actions are custom links or scripts that you can run on specific fields or events. They are also not specific to the CIM add-on, although some Splunk apps may provide their own workflow actions. tsidx files are index files that store the terms and pointers to the raw data in Splunk buckets. They are part of the Splunk indexing process and have nothing to do with the CIM add-on.

If a search returns statistics, it can be viewed as a chart2. Statistics are tabular data that show the relationship between two or more fields2. You can create statistics by using commands such as stats, chart or timechart2. You can view statistics as a chart by selecting the Visualization tab in the Search app and choosing a chart type such as column, line or pie2. Therefore, option B is correct, while options A, C and D are incorrect because they are not types of data that can be viewed as a chart.

The transaction command is used to group events that are related by some common fields or conditions, such as start/end values, time span, or pauses. The stats command is used to calculate statistics on a group of events by a common field value.

References

Splunk Community

Splunk Transaction - Exact Details You Need

A report is a way to save a search and its results in a format that you can reuse and share with others2. A report is also a type of knowledge object, which is an entity that you create to add knowledge to your data and make it easier to search and analyze2. Therefore, option A is correct. A report can be scheduled, which means that you can configure it to run at regular intervals and send the results to yourself or others via email or other methods2. Therefore, option B is correct. A report can run a script, which means that you can specify a script file to execute when the report runs and use it to perform custom actions or integrations2. Therefore, option C is correct. Therefore, option D is correct because all of the above statements are true for reports.

The correct answer is B. Calculated fields can be chained together to create more complex fields.

Calculated fields are fields that are added to events at search time by using eval expressions. They can be used to perform calculations with the values of two or more fields already present in those events. Calculated fields can be defined with Splunk Web or in the props.conf file. They can be used in searches, reports, dashboards, and data models like any other extracted field1.

Calculated fields can also be chained together to create more complex fields. This means that you can use a calculated field as an input for another calculated field. For example, if you have a calculated field named total that sums up the values of two fields named price and tax, you can use the total field to create another calculated field named discount that applies a percentage discount to the total field. To do this, you need to define the discount field with an eval expression that references the total field, such as:

discount = total * 0.9

This will create a new field named discount that is equal to 90% of the total field value for each event2.

References:

• About calculated fields
• Chaining calculated fields

https://docs.splunk.com/Documentation/Splunk/8.1.1/SearchReference/ConditionalFunctions

The correct answer is A. | where 10yearAnnerversary=Renewal-MonthYear.

The where command is used to filter the search results based on an expression that evaluates to true or false. The where command can compare two fields, two values, or a field and a value. The where command can also use functions, operators, and wildcards to create complex expressions1.

The syntax for the where command is:

| where

The expression can be a comparison, a calculation, a logical operation, or a combination of these. The expression must evaluate to true or false for each event.

To compare two fields with the where command, you need to use the field names without any quotation marks. For example, if you want to find events where the values for the 10yearAnnerversary field match the values for the Renewal-MonthYear field, you can use the following syntax:

| where 10yearAnnerversary=Renewal-MonthYear

This will return only the events where the two fields have the same value.

The other options are not correct because they use quotation marks around the field names, which will cause the where command to interpret them as string values instead of field names. For example, if you use:

| where ‘10yearAnnerversary’=‘Renewal-MonthYear’

This will return no events because there are no events where the string value ‘10yearAnnerversary’ is equal to the string value ‘Renewal-MonthYear’.

References:

• where command usage

The correct answer is D. An argument can be passed to the inner macro by nesting parentheses.

A search macro is a way to reuse a piece of SPL code in different searches. A search macro can take arguments, which are variables that can be replaced by different values when the macro is called. A search macro can also contain another search macro within it, which is called a nested macro. A nested macro can also take arguments, which can be passed from the outer macro or directly from the search string.

To pass an argument to the inner macro, you need to use parentheses to enclose the argument value and separate it from the outer macro argument. For example, if you have a search macro named outer_macro (1) that contains another search macro named inner_macro (2), and both macros take one argument each, you can pass an argument to the inner macro by using the following syntax:

outer_macro (argument1, inner_macro (argument2))

This will replace the argument1 and argument2 with the values you provide in the search string. For example, if you want to pass “foo” as the argument1 and “bar” as the argument2, you can write:

outer_macro ("foo", inner_macro ("bar"))

This will expand the macros with the corresponding arguments and run the SPL code contained in them.

References:

• Search macro examples
• Use search macros in searches

When using a field value variable with a Workflow Action, the exclamation mark (!) will escape the data. A Workflow Action is a custom action that performs a task when you click on a field value in your search results. A Workflow Action can be configured with various options, such as label name, base URL, URI parameters, post arguments, app context, etc. A field value variable is a placeholder for the field value that will be used to replace the variable in the URL or post argument of the Workflow Action. A field value variable is written as fieldn​ame, where field_name is the name of the field whose value will be used. However, if the field value contains special characters that need to be escaped, such as spaces, commas, etc., you can use the exclamation mark (!) before and after the field value variable to escape the data. For example, if you have a field value variable host, you can write it as !$host! to escape any special characters in the host field value.

Therefore, option B is the correct answer.

Event types in Splunk are saved searches that categorize data, making it easier to search for specific patterns or criteria within your data. When saving an event type, the search must essentially filter events based on criteria without performing operations that transform or aggregate the data. Here's a breakdown of the options:

A. The search index-server_472 sourcetype-BETA_494 code-488 | stats count by code performs an aggregation operation (stats count by code), which makes it unsuitable for saving as an event type. Event types are meant to categorize data without aggregating or transforming it.

B. The search index=server_472 sourcetype=BETA_494 code=488 [ | inputlookup append=t servercode.csv] includes a subsearch and input lookup, which is typically used to enrich or filter events based on external data. This complexity goes beyond simple event categorization.

C. The search index=server_472 sourcetype=BETA_494 code=488 | stats where code > 200 includes a filtering condition within a transforming command (stats), which again, is not suitable for defining an event type due to the transformation of data.

D. The search index=server_472 sourcetype=BETA_494 code-488 is the correct answer as it purely filters events based on index, sourcetype, and a code field condition without transforming or aggregating the data. This is what makes it suitable for saving as an event type, as it categorizes data based on specific criteria without altering the event structure or content.

The timeline is a graphical representation of your search results that shows the distribution of events over time2. You can use the timeline to zoom in or out of a specific time range or to select one or more bars on the timeline to filter your results by that time range2. However, these actions will not re-run the search, but rather refine the existing results based on the selected time range2. Therefore, options B, C and D are correct, while option A is incorrect because zooming out will re-run the search with a broader time range.

During the validation step of the Field Extractor workflow, you can remove values that aren’t a match for the field you want to define2. The validation step allows you to review and edit the values that have been extracted by the FX and make sure they are correct and consistent2. You can remove values that aren’t a match by clicking on them and selecting Remove Value from the menu2. This will exclude them from your field extraction and update the regular expression accordingly2. Therefore, option A is correct, while options B and C are incorrect because they are not actions that you can perform during the validation step of the Field Extractor workflow.

When using the Field Extractor (FX) in Splunk for regex field extraction, it's important to select the context in which you want to perform the extraction. The context is essentially the subset of data you're focusing on for your field extraction task.

D. Sourcetype or source: This is the correct option. In the initial steps of using the Field Extractor tool, you're prompted to choose a data type for your field extraction. The options available are typically based on the nature of your data and how it's organized in Splunk. "Sourcetype" refers to the kind of data you're dealing with, a categorization that helps Splunk apply specific processing rules. "Source" refers to the origin of the data, like a specific log file or data input. By selecting either a sourcetype or source, you're narrowing down the dataset on which you'll perform the regex extraction, making it more manageable and relevant.

The Field Extractor (FX) is a tool that helps you extract fields from your events using a graphical interface or by manually editing the regular expression2. The FX allows you to create field extractions that persist as knowledge objects, which are entities that you create to add knowledge to your data and make it easier to search and analyze2. Field extractions are methods that extract fields from your raw data using various techniques such as regular expressions, delimiters or key-value pairs2. When you create a field extraction using the FX, you can save it as a knowledge object that applies to your data at search time2. You can also manage and share your field extractions with other users in your organization2. Therefore, option C is correct, while options A, B and D are incorrect because they do not describe the use of the FX.

Using the export function, you can export search results as XML or JSON2. The export function allows you to save your search results in a structured format that can be used by other applications or tools2. You can use the output_mode parameter to specify whether you want to export your results as XML or JSON2. Therefore, options A and B are correct, while options C and D are incorrect because they are not formats that you can export your search results as.

Tags are a type of knowledge object that enable you to assign descriptive keywords to events. Tags can reference any of the following knowledge objects: extracted fields, field aliases, calculated fields, lookups, and event types. Tags cannot reference other tags or search macros. Tags are applied to events at search time based on the values of the fields that they reference2

1: Splunk Core Certified Power User Track, page 10. 2: Splunk Documentation, About tags and aliases.