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

Answer : C

The correct search to generate a field with a value of 'hello' is:

Copy

1

| makeresults | eval field='hello'

Here's why this works:

makeresults : This command creates a single event with no fields.

eval : The eval command is used to create or modify fields. In this case, it creates a new field named field and assigns it the value 'hello'.

Example:

| makeresults

| eval field='hello'

This will produce a result like:

_time field

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

<current_timestamp> hello

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

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

Answer : B

Comprehensive and Detailed Step by Step

The case function can be used as an alternative to coalesce to return the first non-null value. While coalesce(field1, field2, field3) will return the first non-null value, case(condition1, value1, condition2, value2, ...) allows more flexibility by evaluating conditions.

Splunk Documentation - case Function

Answer : A

Comprehensive and Detailed Step by Step

Summary indexing should be used for reports that run on small datasets over long time ranges . It is particularly useful when you need to aggregate data over extended periods without querying raw events repeatedly.

Here's why this works:

Efficiency : Summary indexing pre-aggregates data into summary indexes, reducing the amount of data that needs to be processed during runtime. This improves performance for reports that span long time ranges.

Small Datasets : Summary indexing is most effective when working with smaller datasets because aggregating large volumes of data can become resource-intensive.

Other options explained:

Option B : Incorrect because summary indexing is not a fallback for reports that fail to qualify for acceleration methods like report or data model acceleration.

Option C : Incorrect because summary indexing is less beneficial for short time ranges, where querying raw data is often faster.

Option D : Incorrect because Smart Mode is unrelated to summary indexing; it is a search optimization feature.

Example: Suppose you want to calculate daily sales totals over a year. Instead of querying raw sales data every time, you can use summary indexing to store daily totals and query the summary index instead.

Splunk Documentation on Summary Indexing: https://docs.splunk.com/Documentation/Splunk/latest/Knowledge/Usesummaryindexing

Splunk Documentation on Report Acceleration: https://docs.splunk.com/Documentation/Splunk/latest/Knowledge/Acceleratedatamodels

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 : B

To disable the inspect button on a dashboard panel, set the link.inspect.visible attribute to 0. This hides the button, preventing users from accessing the search inspector for that panel.

To disable the Inspect button on a dashboard panel in Splunk, you need to set the attribute link.inspect.visible to 0. This hides the Inspect button for that specific panel.

Here's why this works:

Purpose of link.inspect.visible : The link.inspect.visible attribute controls the visibility of the Inspect button in a dashboard panel. Setting it to 0 disables the button, while setting it to 1 (default) keeps it visible.

Customization : This is useful when you want to restrict users from inspecting the underlying search queries or data for a specific panel.

Answer : B

The keepevicted parameter in the transaction command controls whether evicted transactions are included in the search results. Evicted transactions are those that were not completed within specified constraints like maxspan, maxpause, or maxevents.

According to Splunk Documentation:

'keepevicted: Whether to output evicted transactions. Evicted transactions can be distinguished from non-evicted transactions by checking the value of the 'closed_txn' field.'

'The 'closed_txn' field is set to '0' for evicted transactions and '1' for closed transactions.'

By setting keepevicted=true, you ensure that these incomplete or failed transactions are included in your search results, allowing for comprehensive analysis.

Answer : A

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

tstats - Splunk Documentation