[Aug 30, 2026] SPLK-1002 Practice Exam Dumps – 99% Marks In Splunk Exam [Q159-Q173]

Event types are created from searches, not field extraction.
Extract: “You can create event types by running a search and saving it as an event type, or by using Settings > Event types > New Event Type.” The Field Extractor is used for field extractions, not for creating event types. Therefore, option C is correct.

Explanation
The stats command is used to calculate summary statistics for your search results such as count, sum, avg, min, max and more2. The stats command supports various functions that you can use to perform calculations on your fields2. However, addtotals is not a stats function but a separate command that adds a row or column with the total of the values in each group2. Therefore, option B is correct, while options A, C and D are incorrect because they are valid stats functions.

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

Reference:
A tag is a descriptive label that you can apply to one or more fields or field values in your events2. You can use tags to simplify your searches by replacing long or complex field names or values with short and simple tags2. To search for a tag associated with a value on a specific field, you can use the following syntax: tag::<field>=<tagname>2. For example, tag::status=error will search for events where the status field has a tag named error. Therefore, option D is correct, while options A, B and C are incorrect because they do not follow the correct syntax for searching tags.

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.

Tags are a type of knowledge object that enable you to assign descriptive keywords to events based on the
values of their fields. Tags can help you to search more efficiently for groups of event data that share common
characteristics, such as functionality, location, priority, etc. For example, you can tag all the IP addresses of
your routers as router, and then search for tag=router to find all the events related to your routers. Tags can
also help you to normalize data from different sources by using the same tag name for equivalent field
values. For example, you can tag the field values error, fail, and critical as severity=high, and then search for
severity=high to find all the events with high severity level2
1: Splunk Core Certified Power User Track, page 10. 2: Splunk Documentation, About tags and aliases.

The timechart command in Splunk requires the _time field to be present in the dataset because it uses time as the primary axis for charting data. The _time field represents the time of events and is essential for commands that generate visualizations based on time, such as timechart. This command groups the events into time intervals and performs statistical functions on those time intervals. Without the _time field, the timechart command will not function properly.
Reference:
Splunk Docs – timechart command

Reference: https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/Definesearchmacros The macro definition below shows a macro that tracks user sessions based on two arguments: action and JSESSIONID.
sessiontracker(2)
The macro definition does the following:
It specifies the name of the macro as sessiontracker. This is the name that will be used to execute the macro in a search string.
It specifies the number of arguments for the macro as 2. This indicates that the macro takes two arguments when it is executed.
It specifies the code for the macro as index=main sourcetype=access_combined_wcookie action=$action$ JSESSIONID=$JSESSIONID$ | stats count by JSESSIONID. This is the search string that will be run when the macro is executed. The search string can contain any part of a search, such as search terms, commands, arguments, etc. The search string can also include variables for the arguments using dollar signs around them.
In this case, action and JSESSIONID are variables for the arguments that will be replaced by their values when the macro is executed.
Therefore, to correctly configure the macro, you should enter sessiontracker as the name and action, JSESSIONID as the arguments. Alternatively, you can use sessiontracker(2) as the name and leave the arguments blank.

Explanation
The search below would limit an “alert” tag to the “host” field.
tag::host=alert
The search does the following:
It uses tag syntax to filter events by tags. Tags are custom labels that can be applied to fields or field values to provide additional context or meaning for your data.
It specifies tag::host=alert as the tag filter. This means that it will only return events that have an “alert” tag applied to their host field or host field value.
It uses an equal sign (=) to indicate an exact match between the tag and the field or field value.

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

Reference:https://www.edureka.co/blog/splunk-events-event-types-and-tags/
As mentioned before, an event type is a way to categorize events based on a search string that matches the
events2. Event types can be tagged, which means that you can apply descriptive labels to event types and use
them in your searches2. Therefore, option A is correct. Event types categorize events based on a search string,
which means that you can define an event type by specifying a search string that matches the events you want
to include in the event type2. Therefore, option C is correct. Event types can be a useful method for capturing
and sharing knowledge, which means that you can use event types to organize your data into meaningful
categories and share them with other users in your organization2. Therefore, option D is correct. Event types
do not have to include a time range, which means that you can create an event type without specifying a time
range for the events2. Therefore, option B is incorrect.

Reference:https://docs.splunk.com/Documentation/SplunkCloud/8.0.2003/Knowledge/Defineeventtypes

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 forthose 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.

Explanation
The following statements about tags are true: tags are based on field/value pairs and tags categorize events based on a search. Tags are custom labels that can be applied to fields or field values to provide additional context or meaning for your data. Tags can be used to filter or analyze your data based on common concepts or themes. Tags can be created by using various methods, such as search commands, configuration files, user interfaces, etc. Some of the characteristics of tags are:
Tags are based on field/value pairs: This means that tags are associated with a specific field name and a specific field value. For example, you can create a tag called “alert” for the field name “status” and the field value “critical”. This means that only events that have status=critical will have the “alert” tag applied to them.
Tags categorize events based on a search: This means that tags are defined by a search string that matches the events that you want to tag. For example, you can create a tag called “web” for the search string sourcetype=access_combined. This means that only events that match the search string sourcetype=access_combined will have the “web” tag applied to them.
The following statements about tags are false: tags are case-insensitive and tags are designed to make data more understandable. Tags are case-sensitive and tags are designed to make data more searchable. Tags are case-sensitive: This means that tags must match the exact case of the field name and field value that they are associated with. For example, if you create a tag called “alert” for the field name “status” and the field value
“critical”, it will not apply to events that have status=CRITICAL or Status=critical. Tags are designed to make data more searchable: This means that tags can help you find relevant events or patterns in your data by using common concepts or themes. For example, if you create a tag called “web” for the search string sourcetype=access_combined, you can use tag=web to find all events related to web activity.

The eval command ‘if’ function requires the following three arguments (in order): boolean expression, result if
true, result if false. The eval command is a search command that allows you to create new fields or modify
existing fields by performing calculations or transformations on them. The eval command can use various
functions to perform different operations on fields. The ‘if’ function is one of the functions that can be used
with the eval command to perform conditional evaluations on fields. The ‘if’ function takes three arguments: a
boolean expression that evaluates to true or false, a result that will be returned if the boolean expression is true,
and a result that will be returned if the boolean expression is false. The ‘if’ function returns one of the two
results based on the evaluation of the boolean expression.