SPLK-1002 Splunk Core Certified Power User Exam Free Practice Exam Questions (2025 Updated)

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.

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 maxspan function of the transaction command allows you to set the maximum total time between the earliest and latest events returned. The maxspan function is an argument that can be used with the transaction command to specify the start and end constraints for the transactions. The maxspan function takes a time modifier as its value, such as 30s, 5m, 1h, etc. The maxspan function sets the maximum time span between the first and last events in a transaction. If the time span between the first and last events exceeds the maxspan value, the transaction will be split into multiple transactions.

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.

Macros in Splunk are defined with the number of arguments inside parentheses after the macro name. For two arguments, the syntax is samplemacro(2). This defines a macro accepting two arguments.

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.

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 format for naming a macro with multiple arguments is monthly_sales3. The square brackets indicate that the macro has arguments, and the number indicates how many arguments it has. The arguments are separated by commas when calling the macro, such as monthly_sales[region,salesperson,date].

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.

 There are several ways to access the field extractor. The option that automatically identifies data type, source type, and sample event is Fields sidebar > Extract New Field. The field extractor is a tool that helps you extract fields from your data using delimiters or regular expressions. The field extractor can generate a regex for you based on your selection of sample values or you can enter your own regex in the field extractor. The field extractor can be accessed by using various methods, such as:

Fields sidebar > Extract New Field: This is the easiest way to access the field extractor. The fields sidebar is a panel that shows all available fields for your data and their values. When you click on Extract New Field in the fields sidebar, Splunk will automatically identify the data type, source type, and sample event for your data based on your current search criteria. You can then use the field extractor to select sample values and generate a regex for your new field.

Event Actions > Extract Fields: This is another way to access the field extractor. Event actions are actions that you can perform on individual events in your search results, such as viewing event details, adding to report, adding to dashboard, etc. When you click on Extract Fields in the event actions menu, Splunk will use the current event as the sample event for your data and ask you to select the source type and data type for your data. You can then use the field extractor to select sample values and generate a regex for your new field.

Settings > Field Extractions > New Field Extraction: This is a more advanced way to access the field extractor. Settings is a menu that allows you to configure various aspects of Splunk, such as indexes, inputs, outputs, users, roles, apps, etc. When you click on New Field Extraction in the Settings menu, Splunk will ask you to enter all the details for your new field extraction manually, such as app context, name, source type, data type, sample event, regex, etc. You can then use the field extractor to verify or modify your regex for your new field.