SPLK-1004 Practice Exam Questions with Answers Splunk Core Certified Advanced Power User Exam Certification

The _time field is required for event annotations in Splunk. This field specifies the time point or range where the annotation should be applied, helping correlate annotations with the correct temporal data.

Contextual drilldown allows values from user clicks to be passed into another dashboard or external page, making dashboards interactive and responsive to user input.

Accelerated data models in Splunk create summaries of data that can be queried more efficiently, significantly improving dashboard performance. By precomputing and storing results, dashboards can retrieve data faster, reducing load times and resource consumption.

According to Splunk Documentation:

"Data model acceleration speeds up reporting for the entire set of fields that you define in a data model and which you and your Pivot users want to report on."

The strftime function in Splunk is used to format timestamps into human-readable strings. When you use strftime(_time, "%H"), it converts the _time field into the hour (00 to 23) based on the user's time zone preference setting.

Splunk stores all timestamps in Coordinated Universal Time (UTC). However, when displaying time, it adjusts according to the user's time zone preference set in their profile. Therefore, using strftime will reflect the local time for the user.

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.

The strftime function in Splunk is used to convert epoch time into a human-readable string format. It takes an epoch time value and a format string as arguments and returns the time as a formatted string. Other options, like strptime, convert string representations of time into epoch format, while tostring converts values to strings, and tonumber converts values to numbers.

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.

In Splunk, the "base lispy" is an internal representation of the search query used by the Search Job Inspector. It breaks down the search into its fundamental components for processing. For the search index=sales clientip=170.192.178.10, Splunk tokenizes the IP address into its individual octets and combines them with the index specification.

Therefore, the base lispy representation would be:

[ index::sales 192 AND 10 AND 178 AND 170 ]

This indicates that the search is constrained to the sales index and is looking for events containing all the specified IP address components.

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.

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.

The correct syntax to return events from between 2:00 AM and 5:00 AM is earliest=-2h@h AND latest=-5h@h. This uses relative time modifiers to specify a range starting at 2 AM and ending at 5 AM.

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.

Form inputs in Splunk dashboards allow users to dynamically interact with the data displayed in panels. When a panel uses an inline search, you can use tokens to replace parts of the search query with values provided by form inputs.

Here’s how this works:

Tokens: Tokens are placeholders in a search query that can be dynamically replaced with user-provided values from form inputs (e.g., dropdowns, text boxes).

Dynamic Searches: When a user interacts with a form input, the token value is updated, and the search query is re-executed with the new value.

Inline Searches: Inline searches are defined directly within the panel's XML or configuration, and they can include tokens to make them dynamic.

For example:

Select Product

Product A

Product B

<table>

Other options explained:

Option A: Incorrect because form inputs can indeed impact panels using inline searches.

Option B: Incorrect because adding a form input does not automatically convert panels to prebuilt panels.

Option D: Incorrect because panels using inline searches do not require a minimum of one form input.

To create a Log Event alert action in Splunk, a power user needs the edit_alerts capability. This capability allows the user to configure and manage alert actions within Splunk.

When a nested macro expands to a search string that begins with a generating command, square brackets are required to ensure proper interpretation. Square brackets allow the nested macro to be treated as a subsearch or command.

When Splunk encounters repeating JSON data structures in an event, they are extracted as multivalue fields. These allow multiple values to be stored under a single field, which is common with arrays in JSON data.

When Splunk extracts repeating JSON data structures within a single event, it represents them asmultivalue fields. A multivalue field is a field that contains multiple values, which can be iterated over or expanded using commands likemvexpandorforeach.

Here’s why this works:

JSON Data Extraction: Splunk automatically parses JSON data into fields. If a JSON key has an array of values (e.g.,"products": ["productA", "productB", "productC"]), Splunk creates a multivalue field for that key.

Multivalue Fields: These fields allow you to handle multiple values for the same key within a single event. For example, if the JSON keyproductscontains an array of product names, Splunk will store all the values in a single multivalue field namedproducts.

{

"event": "purchase",

"products": ["productA", "productB", "productC"]

}

Setting summariesonly=false in the tstats command retrieves results from both summarized (accelerated) and non-summarized (raw) data, allowing a more comprehensive analysis of both types of data in the same query.

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.

Self-describing data includes information about its structure within the data itself. Examples include formats like JSON and XML, where the data schema is embedded and can be easily interpreted without external references.

Comprehensive and Detailed Step by Step Explanation:

To build acontextual drilldownin Splunk dashboards, you can use<set>and<unset>elements with adepend?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. Thedepend?attribute ensures that the behavior is conditional and context-aware.

Example:

$click.value$

In this example:

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

If the condition specified independ?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, andrejectsis unrelated to drilldown behavior.

Option D: Incorrect because<offset>is not used for building drilldowns, anddepends/rejectsdo not apply in this context.

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.

In Splunk, when a user accesses a dataset in Pivot that lacks persistent acceleration, Splunk automatically creates anad hoc data model acceleration. This temporary acceleration is designed to enhance performance during the user's current session.

According to Splunk Documentation:

"Ad hoc summaries are always created in a dispatch directory at the search head."

"These summaries are temporary and exist only for the duration of the user's Pivot session."

This means that the accelerated data model persists only while the user is actively engaged in the Pivot session. Once the session ends, the ad hoc acceleration is discarded.

The | makeresults command generates a single event containing default fields, such as _time. It's mainly used to create sample data or placeholder events for testing purposes. The primary field it generates is _time, but the command is used to generate a base event that can be manipulated further.

Comprehensive and Detailed Step by Step Explanation:

A webhook in Splunk is designed to send HTTP POST requests to a specified URL when an alert is triggered. This mechanism allows Splunk to communicate with external systems by pushing data to them.Common use cases for webhooks include:

Creating a ticket in a support application:By sending a POST request to the support application's API endpoint with the necessary details, a new ticket can be created automatically.

Posting a notification on a web page:If the web page has an API that accepts POST requests, Splunk can send data to it, resulting in a notification being displayed.

Posting a message in a chatroom:Many chat platforms offer webhook integrations where POST requests can send messages to specific channels or chatrooms.

However,retrieving data from a web pageis not within the capabilities of a webhook. Webhooks are designed for outbound communication (sending data) and do not handle inbound requests or data retrieval. To fetch or retrieve data from external sources, other methods such as scripted inputs or custom scripts would be required.

The xyseries command in Splunk transforms a stats-like output into a chart-like output, making it easier to visualize complex relationships between multiple data points.

Comprehensive and Detailed Step by Step Explanation:

Theuntablecommand in Splunk converts tabular data (rows and columns) into a format where each row represents a key-value pair. Its opposite is thechartcommand, which aggregates data into a tabular format with rows and columns.

Here’s whychartis the opposite ofuntable:

untable: This command takes structured data (e.g., a table with columnsA,B,C) and transforms it into a long format where each row contains a key-value pair (e.g.,field,value).

chart: This command aggregates data into a structured table format, grouping data by specified fields and calculating statistics (e.g., count, sum).

Example: Usinguntable:

spl

Copy

1

| untable _time field value

This converts a table into key-value pairs.

Usingchart:

spl

Copy

1

| chart count by field

This aggregates data into a structured table.

Other options explained:

Option B: Incorrect becausetablesimply selects specific fields for display but does not aggregate data likechart.

Option C: Incorrect becausebinis used for bucketing numeric or time-based data, not for creating tables.

Option D: Incorrect becausexyseriestransforms data into a series format but does not directly reverse the effect ofuntable.

The recommended way to create a field extraction that is both persistent and precise is to use the Field Extractor and manually edit the generated regular expression. This ensures accuracy and allows for customization beyond the automatically generated regex.

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.

The transaction command requires events in ascending chronological order to group related events correctly into meaningful transactions.

In Splunk, a lookup can be referenced in an alert by running a search that incorporates the lookup and saving that search as an alert. This allows the alert to use the lookup data as part of its logic.

In Splunk Simple XML for dashboards, the element is used within a configuration to pass multiple fields to another dashboard using dynamic drilldown.