If a search contains a subsearch, what is the order of execution?
B
Explanation:
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.
Reference:
Splunk Documentation on Subsearches:
https://docs.splunk.com/Documentation/Splunk/latest/Search/Aboutsubsearches
Splunk Documentation on Search Syntax:
https://docs.splunk.com/Documentation/Splunk/latest/Search/Usefieldsinsearches
How can the erex and rex commands be used in conjunction to extract fields?
A
Explanation:
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.
What command is used to compute and write summary statistics to a new field in the event results?
C
Explanation:
The eventstats command in Splunk is used to compute and add summary statistics to all events in the
search results, similar to stats, but without grouping the results into a single event.
Which commands can run on both search heads and indexers?
D
Explanation:
In Splunk's processing model, commands are categorized based on how and where they execute
within the search pipeline. Understanding these categories is crucial for optimizing search
performance.
Distributable Streaming Commands:
Definition: These commands operate on each event individually and do not depend on the context of
other events. Because of this independence, they can be executed on indexers, allowing the
processing load to be distributed across multiple nodes.
Execution: When a search is run, distributable streaming commands can process events as they are
retrieved from the indexers, reducing the amount of data sent to the search head and improving
efficiency.
Examples: eval, rex, fields, rename
Other Command Types:
Dataset Processing Commands: These commands work on entire datasets and often require all
events to be available before processing can begin. They typically run on the search head.
Centralized Streaming Commands: These commands also operate on each event but require a
centralized view of the data, meaning they usually run on the search head after data has been
gathered from the indexers.
Transforming Commands: These commands, such as stats or chart, transform event data into
statistical tables and generally run on the search head.
By leveraging distributable streaming commands, Splunk can efficiently process data closer to its
source, optimizing resource utilization and search performance.
Reference:
Splunk Documentation: Types of commands
What is returned when Splunk finds fewer than the minimum matches for each lookup value?
A
Explanation:
When Splunk's lookup feature finds fewer than the minimum matches for each lookup value, it
returns the default value NULL for unmatched entries until the minimum match threshold is reached.
When would a distributable streaming command be executed on an indexer?
C
Explanation:
A distributable streaming command would be executed on an indexer if all preceding search
commands are executed on the indexer, enhancing search efficiency by processing data where it
resides.
A distributable streaming command is executed on an indexer if all preceding search commands are
executed on the indexer . This ensures that the entire pipeline up to that point can be processed
locally on the indexer without requiring intermediate results to be sent to the search head.
Here’s why this works:
Distributable Streaming Commands : These commands process data in a streaming manner and can
run on indexers if all prior commands in the pipeline are also distributable. Examples include eval,
fields, and rex.
Execution Location : For a command to execute on an indexer, all preceding commands must also be
distributable. If any non-distributable command (e.g., stats, transaction) is encountered, processing
shifts to the search head.
Why is the transaction command slow in large Splunk deployments?
C
Explanation:
The transaction command can be slow in large deployments because it requires all event data
relevant to the transaction to be returned to the search head, which can be resource-intensive.
What are the four types of event actions?
C
Explanation:
The four types of event actions in 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 because stats and target are not valid event actions.
Option B : Incorrect because set and unset are not valid event actions.
Option D : Incorrect because stats and target are not valid event actions.
Reference:
Splunk Documentation on Event Actions:
https://docs.splunk.com/Documentation/Splunk/latest/Viz/EventActions
Splunk Documentation on Dashboard Interactivity:
https://docs.splunk.com/Documentation/Splunk/latest/Viz/PanelreferenceforSimplifiedXML
When using the bin command, which argument sets the bin size?
D
Explanation:
In Splunk, the span argument is used to set the size of each bin when using the bin command,
determining the granularity of segmented data over a time range or numerical field.
How is a cascading input used?
C
Explanation:
A cascading input is used to filter other input selections in a dashboard or form, allowing for a
dynamic user interface where one input influences the options available in another input.
Cascading Inputs:
Definition: Cascading inputs are interconnected input controls in a dashboard where the selection in
one input filters the options available in another. This creates a hierarchical selection process,
enhancing user experience by presenting relevant choices based on prior selections.
Implementation:
Define Input Controls:
Create multiple input controls (e.g., dropdowns) in the dashboard.
Set Token Dependencies:
Configure each input to set a token upon selection.
Subsequent inputs use these tokens to filter their available options.
Example:
Consider a dashboard analyzing sales data:
Input 1: Country Selection
Dropdown listing countries.
Sets a token $country$ upon selection.
Input 2: City Selection
Dropdown listing cities.
Uses the $country$ token to display only cities within the selected country.
XML Configuration:
<input type="dropdown" token="country">
<label>Select Country</label>
<choice value="USA">USA</choice>
<choice value="Canada">Canada</choice>
</input>
<input type="dropdown" token="city">
<label>Select City</label>
<search>
<query>index=sales_data country=$country$ | stats count by city</query>
</search>
</input>
In this setup:
Selecting a country sets the $country$ token.
The city dropdown's search uses this token to display cities relevant to the selected country.
Benefits:
Improved User Experience: Users are guided through a logical selection process, reducing the chance
of invalid or irrelevant selections.
Data Relevance: Ensures that dashboard panels and visualizations reflect data pertinent to the user's
selections.
Other Options Analysis:
B . As part of a dashboard, but not in a form:
Cascading inputs are typically used within forms in dashboards to collect user input. This option is
incorrect as it suggests a limitation that doesn't exist.
C . Without token notation in the underlying XML:
Cascading inputs rely on tokens to pass values between inputs. Therefore, token notation is essential
in the XML configuration.
D . As a default way to delete a user role:
This is unrelated to the concept of cascading inputs.
Conclusion:
Cascading inputs are used in dashboards to create a dependent relationship between input controls,
allowing selections in one input to filter the options available in another, thereby enhancing data
relevance and user experience.
Reference:
Splunk Documentation: Set up cascading or dependent inputs
Which of the following is accurate regarding predefined drilldown tokens?
B
Explanation:
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.
Which of the following statements is accurate regarding the append command?
B
Explanation:
The append command in Splunk is used with a subsearch to add additional data to the end of the
primary search results and can access historical data, making it useful for combining datasets from
different time ranges or sources.
What happens to panels with post-processing searches when their base search is refreshed?
C
Explanation:
When the base search of a dashboard panel with post-processing searches is refreshed, the panels
with these post-processing searches are refreshed automatically to reflect the updated data.
Which of the following are potential string results returned by the typeof function?
B
Explanation:
The typeof function 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 : The typeof function is commonly used in conjunction with the eval command 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, or Bool.
Example:
| makeresults
| eval example_field = "123"
| eval type = typeof(example_field)
This will produce:
_time
example_field type
------------------- -------------- ------
<current_timestamp> 123
String
Other options explained:
Option A : Incorrect because True, False, and Unknown are not valid return values of the typeof
function. These might be confused with Boolean logic but are not related to data type identification.
Option C : Incorrect because Null is not a valid return value of typeof. Instead, Null represents the
absence of a value, not a data type.
Option D : Incorrect because Field, Value, and Lookup are unrelated to the typeof function. These
terms describe components of Splunk searches, not data types.
Reference:
Splunk Documentation on typeof:
https://docs.splunk.com/Documentation/Splunk/latest/SearchReference/CommonEvalFunctions
Splunk Documentation on Data Types:
https://docs.splunk.com/Documentation/Splunk/latest/Search/Aboutfields
Which search generates a field with a value of "hello"?
C
Explanation:
The correct search to generate a field with a value of "hello" is:
Copy
| 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
Reference:
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