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

Answer : A

Comprehensive and Detailed Step by Step

The predefined tokens in Splunk include $earliest_tok$ and $now$. These tokens are automatically available for use in searches, dashboards, and alerts.

Here's why this works:

Predefined Tokens :

$earliest_tok$: Represents the earliest time in a search's time range.

$now$: Represents the current time when the search is executed.

These tokens are commonly used to dynamically reference time ranges or timestamps in Splunk queries.

Dynamic Behavior : Predefined tokens like $earliest_tok$ and $now$ are automatically populated by Splunk based on the context of the search or dashboard.

Other options explained:

Option B : Incorrect because ?click.field? and ?click.value? are not predefined tokens; they are contextual drilldown tokens that depend on user interaction.

Option C : Incorrect because ?earliest_tok$ and ?latest_tok? mix invalid syntax (? and $) and are not predefined tokens.

Option D : Incorrect because ?click.name? and ?click.value? are contextual drilldown tokens, not predefined tokens.

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

Splunk Documentation on Time Tokens: https://docs.splunk.com/Documentation/Splunk/latest/Search/Specifytimemodifiersinyoursearch

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 clears the token when the condition is no longer valid.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 : D

Comprehensive and Detailed Step by Step

The multikv command in Splunk is used to extract fields from table-like events (e.g., logs with rows and columns). It creates a separate event for each row in the table, making it easier to analyze structured data.

Here's why this works:

Purpose of multikv : The multikv command parses table-formatted events and treats each row as an individual event. This allows you to work with structured data as if it were regular Splunk events.

Field Extraction : By default, multikv extracts field names from the header row of the table and assigns them to the corresponding values in each row.

Row-Based Events : Each row in the table becomes a separate event, enabling you to search and filter based on the extracted fields.

Example: Suppose you have a log with the following structure:

Name Age Location

Alice 30 New York

Bob 25 Los Angeles

Using the multikv command:

| multikv

This will create two events:

Event 1: Name=Alice, Age=30, Location=New York

Event 2: Name=Bob, Age=25, Location=Los Angeles

Other options explained:

Option A : Incorrect because multikv derives field names from the header row, not the last column.

Option B : Incorrect because multikv creates events for rows, not columns.

Option C : Incorrect because multikv does not require field names to be in ALL CAPS, regardless of the multitable setting.

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

Splunk Documentation on Parsing Structured Data: https://docs.splunk.com/Documentation/Splunk/latest/Data/Extractfieldsfromstructureddata

Answer : A

Comprehensive and Detailed Step by Step

When working with nested macros in Splunk, the inner macro should be created first . This ensures that the outer macro can reference and use the inner macro correctly during execution.

Here's why this works:

Macro Execution Order : Macros are processed in a hierarchical manner. The inner macro is executed first, and its output is then passed to the outer macro for further processing.

Dependency Management : If the inner macro does not exist when the outer macro is defined, Splunk will throw an error because the outer macro cannot resolve the inner macro's definition.

Other options explained:

Option B : Incorrect because the outer macro depends on the inner macro, so the inner macro must be created first.

Option C : Incorrect because macro names are referenced using dollar signs ($macro_name$), not backticks. Backticks are used for inline searches or commands.

Option D : Incorrect because arguments are passed to the inner macro, not the other way around. The inner macro processes the arguments and returns results to the outer macro.

Example:

# Define the inner macro

[inner_macro(1)]

args = arg1

definition = eval result = $arg1$ * 2

# Define the outer macro

[outer_macro(1)]

args = arg1

definition = `inner_macro($arg1$)`

In this example, inner_macro must be defined before outer_macro.

Splunk Documentation on Macros: https://docs.splunk.com/Documentation/Splunk/latest/Knowledge/Definesearchmacros

Splunk Documentation on Nested Macros: https://docs.splunk.com/Documentation/Splunk/latest/Search/Usesearchmacros

Answer : A

Comprehensive and Detailed Step by Step

The default time and results limits for a subsearch in Splunk are:

Time Limit : 60 seconds

Results Limit : 10,000 results

Here's why this works:

Time Limit : Subsearches are designed to execute quickly to avoid performance bottlenecks. By default, Splunk imposes a timeout of 60 seconds for subsearches. If the subsearch exceeds this limit, it will terminate, and the outer search may fail.

Results Limit : Subsearches are also limited to returning a maximum of 10,000 results by default. This ensures that the outer search does not get overwhelmed with too much data from the subsearch.

Other options explained:

Option B : Incorrect because the results limit is 10,000, not 50,000.

Option C : Incorrect because the time limit is 60 seconds, not 300 seconds.

Option D : Incorrect because both the time limit (300 seconds) and results limit (50,000) exceed the default values.

Example: If a subsearch exceeds the default limits, you might see an error like:

Copy

1

Error in 'search': Subsearch exceeded configured timeout or result limit.

Splunk Documentation on Subsearch Limits: https://docs.splunk.com/Documentation/Splunk/latest/Search/Aboutsubsearches

Splunk Documentation on limits.conf: https://docs.splunk.com/Documentation/Splunk/latest/Admin/Limitsconf

Answer : B

Comprehensive and Detailed Step by Step

When using a KV Store Collection as a lookup in Splunk, each collection must have at least 2 fields , and one 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 field user_id, and your KV Store Collection has fields user_id and user_name, you can use the lookup command to enrich your events with user_name based on the matching user_id.

Splunk Documentation on KV Store Lookups: https://docs.splunk.com/Documentation/Splunk/latest/Knowledge/ConfigureKVstorelookups

Splunk Documentation on Lookups: https://docs.splunk.com/Documentation/Splunk/latest/Knowledge/Aboutlookupsandfieldactions

Answer : A

Comprehensive and Detailed Step by Step

The streamstats command calculates statistics on search results as each event is processed , maintaining a running total or other cumulative calculations. Unlike eventstats, which calculates statistics for the entire dataset at once, streamstats processes 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 : streamstats applies 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

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

<current_timestamp> 1

<current_timestamp> 2

<current_timestamp> 3

<current_timestamp> 4

<current_timestamp> 5

Other options explained:

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

Option C : Incorrect because eventstats calculates statistics for the entire dataset at once, not incrementally.

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

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

Splunk Documentation on Statistical Commands: https://docs.splunk.com/Documentation/Splunk/latest/SearchReference/StatisticalAggregatingCommands