Splunk Core Certified Advanced Power User SPLK-1004 Exam Practice Test

Answer : B

The fill_summary_index.py script is a utility provided by Splunk to backfill data into a summary index. It's particularly useful when there are gaps in the summary index due to missed scheduled searches or when initializing a summary index with historical data.

According to Splunk Documentation:

'You can use the fill_summary_index.py script, which backfills gaps in summary index collection by running the saved searches that populate the summary index as they would have been executed at their regularly scheduled times for a given time range.'

Answer : B

In Splunk's Simple XML dashboards, token filters modify how token values are rendered. The |s token filter specifically wraps the token value in double quotes and escapes any internal quotation marks. This is particularly useful when constructing search strings that require quoted values.

For example, using $token_name|s$ ensures that the value of token_name is enclosed in double quotes, which is essential when the value contains spaces or special characters.

Answer : C

To create a multivalue field from a single string with comma-separated values, the makemv command is used with the delim parameter to specify the delimiter.

The correct syntax is:

... | makemv delim=',' product

This command splits the product field into multiple values wherever a comma is found, effectively creating a multivalue field.

makemv - Splunk Documentation

Answer : A

Comprehensive and Detailed Step by Step

To build a contextual drilldown in Splunk dashboards, you can use <set> and <unset> elements with a depend? attribute. These elements allow you to dynamically update tokens based on user interactions, enabling context-sensitive behavior in your dashboard.

Here's why this works:

Contextual Drilldown : A contextual drilldown allows users to click on a visualization (e.g., a chart or table) and navigate to another view or filter data based on the clicked value.

Dynamic Tokens : The <set> element sets a token to a specific value when a condition is met, while <unset> clears the token when the condition is no longer valid. The depend? attribute ensures that the behavior is conditional and context-aware.

Example:

<drilldown>

<set token='selected_product'>$click.value$</set>

<unset token='selected_product' depend='?'></unset>

</drilldown>

In this example:

When a user clicks on a value, the selected_product token is set to the clicked value ($click.value$).

If the condition specified in depend? is no longer true, the token is cleared using <unset>.

Other options explained:

Option B : Incorrect because $earliest$ and $latest$ tokens are related to time range pickers, not contextual drilldowns.

Option C : Incorrect because <reset> is not a valid element in Splunk XML, and rejects is unrelated to drilldown behavior.

Option D : Incorrect because <offset> is not used for building drilldowns, and depends/rejects do not apply in this context.

Splunk Documentation on Drilldowns: https://docs.splunk.com/Documentation/Splunk/latest/Viz/DrilldownIntro

Splunk Documentation on Tokens: https://docs.splunk.com/Documentation/Splunk/latest/Viz/UseTokenstoBuildDynamicInputs

Answer : C

The rex and erex commands in Splunk are both used for field extraction, but they differ in their approach and requirements.

According to Splunk Documentation:

'rex: Specify a Perl regular expression named groups to extract fields while you search.'

'erex: Use the erex command to extract data from a field when you do not know the regular expression to use. The command automatically extracts field values that are similar to the example values you specify.'

This indicates that:

The rex command requires users to have knowledge of regular expressions to define the extraction patterns.

The erex command is designed for users who may not be familiar with regular expressions, allowing them to provide example values, and Splunk generates the appropriate regular expression.

Answer : B

Predefined drilldown tokens in Splunk vary by visualization type. These tokens are placeholders that capture dynamic values based on user interactions with dashboard elements, such as clicking on a chart segment or table row. Different visualization types may have different drilldown tokens.

Answer : D

Comprehensive and Detailed Step by Step

Multivalue functions in Splunk are used to manipulate fields that contain multiple values. The correct group of commands that can use multivalue functions is:

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eval, mvexpand, and makemv

Here's why this works:

eval : This command can use multivalue functions like mvappend(), mvcount(), and mvjoin() to manipulate multivalue fields.

mvexpand : This command expands multivalue fields into separate events, making it easier to work with individual values.

makemv : This command splits a single-value field into a multivalue field based on a delimiter.

Other options explained:

Option A : Incorrect because fieldformat is used for formatting display values and does not support multivalue functions.

Option B : Incorrect because fields is used to include or exclude fields but does not handle multivalue fields.

Option C : Incorrect because fieldformat and search do not support multivalue functions.

Example:

| makeresults

| eval products='productA,productB,productC'

| makemv delim=',' products

| mvexpand products

Splunk Documentation on Multivalue Functions: https://docs.splunk.com/Documentation/Splunk/latest/SearchReference/MultivalueEvalFunctions

Splunk Documentation on mvexpand: https://docs.splunk.com/Documentation/Splunk/latest/SearchReference/mvexpand