SPLK-1004 Splunk Core Certified Advanced Power User Exam Free Practice Exam Questions (2025 Updated)

Cascading inputs allow one input's selection to determine the options available in subsequent inputs. An event handler can reset the cascading sequence based on user interactions, ensuring the following inputs reflect appropriate options based on prior selections.

Cascading inputs in Splunk dashboards allow one input to dynamically update or influence another input. These inputs are often used to create dependent dropdowns or filters. One key feature of cascading inputs is that theycan be reset by an event handler.

Here’s why this works:

Cascading Behavior: Cascading inputs are designed to update dynamically based on user selections. For example, selecting a value in one dropdown might populate or filter the options in another dropdown.

Resetting Inputs: Event handlers (e.g.,changeevents) can reset or clear the values of cascading inputs when certain conditions are met. This ensures that the dashboard remains consistent and avoids invalid combinations of inputs.

Dynamic Tokens: Cascading inputs use tokens to pass values between inputs and searches. These tokens can be updated or cleared dynamically using event handlers.

Comprehensive and Detailed Step by Step Explanation:

When using a KV Store Collection as a lookup in Splunk,each collection must have at least 2 fields, andone of these fields must match values of a field in your event data. This matching field serves as the key for joining the lookup data with your search results.

Here’s why this works:

Minimum Fields Requirement: A KV Store Collection must have at least two fields: one to act as the key (matching a field in your event data) and another to provide additional information or context.

Key Matching: The matching field ensures that the lookup can correlate data from the KV Store with your search results. Without this, the lookup would not function correctly.

Other options explained:

Option A: Incorrect because a KV Store Collection does not require at least 3 fields; 2 fields are sufficient.

Option C: Incorrect because at least one field in the collection must match a field in your event data for the lookup to work.

Option D: Incorrect because a KV Store Collection does not require at least 3 fields, and at least one field must match event data.

Example: If your event data contains a fielduser_id, and your KV Store Collection has fieldsuser_idanduser_name, you can use thelookupcommand to enrich your events withuser_namebased on the matchinguser_id.

A report qualifies for acceleration in Splunk if it involves fewer than 100,000 events in the search results and uses transforming commands. Transforming commands aggregate data, which helps reduce the dataset's size and complexity, making the report suitable for acceleration.

In Splunk, a base search is defined using

An event handler action can trigger an eval statement based on a user's interaction with a form. This makes dashboards interactive by allowing real-time updates based on user input, modifying the data presented dynamically.

Thetypeoffunction in Splunk is used to determine the data type of a field or value.It returns one of the following string results:

Number: Indicates that the value is numeric.

String: Indicates that the value is a text string.

Bool: Indicates that the value is a Boolean (true/false).

Here’s why this works:

Purpose of typeof: Thetypeoffunction is commonly used in conjunction with theevalcommand to inspect the data type of fields or expressions. This is particularly useful when debugging or ensuring that fields are being processed as expected.

Return Values: The function categorizes values into one of the three primary data types supported by Splunk:Number,String, orBool.

Example:

| makeresults

| eval example_field = "123"

| eval type = typeof(example_field)

This will produce:

_time example_field type

------------------- -------------- ------

123 String

Other options explained:

Option A: Incorrect becauseTrue,False, andUnknownare not valid return values of thetypeoffunction. These might be confused with Boolean logic but are not related to data type identification.

Option C: Incorrect becauseNullis not a valid return value oftypeof. Instead,Nullrepresents the absence of a value, not a data type.

Option D: Incorrect becauseField,Value, andLookupare unrelated to thetypeoffunction. These terms describe components of Splunk searches, not data types.

The erex command in Splunk generates regular expressions based on example data. These generated regular expressions can then be edited and utilized with the rex command in subsequent searches.

Thespathcommand in Splunk is used to extract fields from structured data formats like JSON or XML.No arguments are requiredfor basic usage, asspathautomatically parses the_rawfield by default.

Here’s why this works:

Default Behavior: By default,spathextracts fields from the_rawfield of events without requiring any arguments. It intelligently parses JSON or XML data and creates new fields based on the structure.

Optional Arguments: Whilespathdoes not require arguments, you can optionally specify:

input: To specify a field other than_rawto parse.

output: To rename the extracted fields.

path: To extract specific subfields within the structured data.

Example:

| makeresults

| eval _raw="{\"name\":\"Alice\",\"age\":30}"

| spath

Fields like date_minute, date_year, and date_day are common default time fields in Splunk, while date_zone is not typically a default field for time-related data.

In a Splunk search containing a subsearch, the inner subsearch executes first. The result of the subsearch is then passed to the outer search, which often depends on the results of the inner subsearch to complete its execution.

In Splunk, the is_exact field indicates whether the count of distinct values for a particular field is exact or estimated. A value of:

1 means the count is exact.

0 means the count is an approximation.

Therefore, when is_exact is 0, it signifies that the distinct count of values for that field is an estimate, not an exact count.

The four types ofevent actionsin Splunk are:

eval: Allows you to create or modify fields using expressions.

link: Creates clickable links that can redirect users to external resources or other Splunk views.

change: Triggers actions when a field's value changes, such as highlighting or formatting changes.

clear: Clears or resets specific fields or settings in the context of an event action.

Here’s why this works:

These event actions are commonly used in Splunk dashboards and visualizations to enhance interactivity and provide dynamic behavior based on user input or data changes.

Other options explained:

Option A: Incorrect becausestatsandtargetare not valid event actions.

Option B: Incorrect becausesetandunsetare not valid event actions.

Option D: Incorrect becausestatsandtargetare not valid event actions.

The regex is passed to the makemv command in Splunk using the delim argument. This argument specifies the delimiter used to split a single string field into multiple values, effectively creating a multivalue field.

The Splunk Job Inspector provides detailed metrics about the execution of search jobs, including the time taken by various components. The component responsible for measuring the time taken to apply field extractions is command.search.kv.

According to Splunk Documentation:

command.search.kv– tells how long it took to apply field extractions to the events.

This component specifically measures the duration of key-value field extraction processes during a search job.

Comprehensive and Detailed Step by Step Explanation:

Thestreamstatscommand calculates statistics on search resultsas each event is processed, maintaining a running total or other cumulative calculations. Unlikeeventstats, which calculates statistics for the entire dataset at once,streamstatsprocesses events sequentially.

Here’s why this works:

Purpose of streamstats: This command is ideal for calculating cumulative statistics, such as running totals, averages, or counts, as events are returned by the search.

Sequential Processing:streamstatsapplies statistical functions (e.g.,count,sum,avg) incrementally to each event based on the order of the results.

| makeresults count=5

| streamstats count as running_count

This will produce:

_time running_count

------------------- -------------

1

2

3

4

5

Other options explained:

Option B: Incorrect becausefieldsummarygenerates summary statistics for all fields in the dataset, not cumulative statistics.

Option C: Incorrect becauseeventstatscalculates statistics for the entire dataset at once, not incrementally.

Option D: Incorrect becauseappendpipeis used to append additional transformations or calculations to existing results, not for cumulative statistics.

Comprehensive and Detailed Step by Step Explanation:

Thesummariesonly=targument of thetstatscommandapplies only to accelerated data models. It ensures that the search uses only the precomputed summaries of the data model, ignoring raw data.

Here’s why this works:

Purpose of summariesonly=t: When set totrue, thetstatscommand restricts the search to use only the accelerated summaries of the data model. This improves performance but may exclude events that are not part of the summary.

Accelerated Data Models: Acceleration creates summaries of data models, making them faster to query. Usingsummariesonly=tensures that only these summaries are queried, avoiding raw data entirely.

Other options explained:

Option B: Incorrect becausesummariesonly=tdoes not apply to unaccelerated data models; it requires acceleration to function.

Option C: Incorrect becausesummariesonly=tapplies only to accelerated data models, not unaccelerated ones.

Option D: Incorrect becausesummariesonly=ttypically produces fewer results, as it excludes raw data that is not part of the summary.

Example:

| tstats count WHERE index=_internal summariesonly=t BY sourcetype

This query uses only the accelerated summaries of the_internalindex.

The Admin role (Option D) has the privilege to use the Log Event alert action, which logs an event to an index when an alert is triggered. Admins have the broadest range of permissions, including configuring and managing alert actions in Splunk.

TheAdminrole in Splunk has the necessary permissions to use theLog Event alert action. This action allows alerts to generate log entries in the_internalindex, which can be useful for auditing or tracking alert activity.

Here’s why this works:

Permissions Required: The Log Event alert action requires administrative privileges because it involves writing data to the_internalindex, which is typically restricted to users with elevated permissions.

Default Roles: By default, only theAdminrole has the required capabilities (edit_roles,schedule_search, andwrite_to_internal_index) to configure and execute this alert action.

A.tsidx(time-series index) file in Splunk consists of two main components:

Lexicon: A dictionary of unique terms (e.g., field names and values) extracted from indexed data.

Posting List: A mapping of terms in the lexicon to the locations (offsets) of events containing those terms.

Here’s why this works:

Purpose of .tsidx Files: These files enable fast searching by indexing terms and their locations in the raw data. They are critical for efficient search performance.

Structure: The lexicon ensures that each term is stored only once, while the posting list links terms to their occurrences in events.

Other options explained:

Option B: Incorrect because Splunk does not remove.tsidxfiles every 5 minutes. These files are part of the index and persist until the associated data is aged out or manually deleted.

Option C: Incorrect because.tsidxfiles are updated as data is indexed, not at fixed intervals like every 30 minutes.

Option D: Incorrect because each bucket can contain multiple.tsidxfiles, depending on the volume of indexed data.

The tstats command in Splunk is optimized for performance and is typically used with accelerated data models. The summariesonly parameter determines whether the search should use only the summarized (accelerated) data or fall back to raw data if necessary.

Setting summariesonly=false allows the search to use both summarized and raw data, making it suitable for both accelerated and unaccelerated data models.

Setting summariesonly=true restricts the search to only summarized data, which would result in no data returned if the data model is not accelerated.

Therefore, to search an accelerated data model and allow fallback to raw data if needed, the correct query is:

| tstats count from datamodel=acc_datmodel summariesonly=false