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[General] SPLK-1004 Test Guide: Splunk Core Certified User & SPLK-1004 Exam Torrent &a

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【General】 SPLK-1004 Test Guide: Splunk Core Certified User & SPLK-1004 Exam Torrent &a

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Splunk SPLK-1004 exam is a certification program designed to validate advanced knowledge and skills in using Splunk for analyzing and visualizing large datasets. SPLK-1004 exam is aimed at Splunk power users who have already completed the Splunk Core Certified User exam and are looking to enhance their expertise in the platform. The Splunk SPLK-1004 exam covers essential topics such as data transformation, data models, field aliases, macros, and regular expressions, which are necessary for analyzing complex data sets in Splunk.
The SPLK-1004 Exam consists of 60 multiple-choice questions to be completed in 90 minutes. SPLK-1004 exam covers a wide range of topics, including advanced search techniques, report and dashboard creation, data models, and troubleshooting. To pass the exam and earn the SPLK-1004 certification, candidates must achieve a minimum score of 70%. Splunk offers a variety of training resources and study materials to help candidates prepare for the exam, including instructor-led courses, online training, and a certification study guide.
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Splunk Core Certified Advanced Power User Sample Questions (Q113-Q118):NEW QUESTION # 113
Which of the following is an event handler action?
  • A. Pass a token from a drilldown to modify index settings.
  • B. Set a token to select a value from the time range picker.
  • C. Run an eval statement based on a user clicking a value on a form.
  • D. Cancel all jobs based on the number of search job results captured.
Answer: C
Explanation:
An event handler action in Splunk is an action that is triggered based on user interaction with dashboard elements. Running an eval statement based on a user clicking a value on a form (Option A) is an example of an event handler action. This capability allows dashboards to be interactive and dynamic, responding to user inputs or actions to modify displayed data, visuals, or other elements in real-time.

NEW QUESTION # 114
What is the correct hierarchy of XML elements in a dashboard panel?
  • A. <panel><row><dashboard>
  • B. <dashboard><row><panel>
  • C. <dashboard><panel><row>
  • D. <panel><dashboard><row>
Answer: B
Explanation:
In a Splunk dashboard, the correct hierarchy of XML elements for a dashboard panel is
<dashboard><row><panel> (Option B). A Splunk dashboard is defined within the <dashboard> element.
Within this, <row> elements are used to organize the layout into rows, and each <panel> element within a row defines an individual panel that can contain visualizations, searches, or other content. This hierarchical structure allows for organized and customizable layouts of dashboard elements, facilitating clear presentation of data and analyses. The other options provided do not represent the correct hierarchical order for defining dashboard panels in Splunk's XML dashboard syntax.

NEW QUESTION # 115
Which commands can run on both search heads and indexers?
  • A. Centralized streaming commands
  • B. Distributable streaming commands
  • C. Dataset processing commands
  • D. Transforming commands
Answer: B
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

NEW QUESTION # 116
Which of the following best describes the process for tokenizing event data?
  • A. The event data has all punctuation stripped out and is then space-delimited.
  • B. The event data is broken up by major breakers and then broken up further by minor breakers.
  • C. The event data is broken up by a series of user-defined regex patterns.
  • D. The event data is broken up by values in the punch field.
Answer: B
Explanation:
The process for tokenizing event data in Splunk involves breaking the event data up by major breakers (which typically identify the boundaries of events) and further breaking it up by minor breakers (which segment the event data into fields). This hierarchical approach allows Splunk to efficiently parse and structure the data.

NEW QUESTION # 117
When should the fill_summary_index.py script be used?
  • A. To create a summary index.
  • B. To backfill gaps in a summary index.
  • C. To reset a summary index that includes overlapping data.
  • D. To populate a summary index from a saved report.
Answer: B
Explanation:
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." Reference:Manage summary index gaps - Splunk Documentation

NEW QUESTION # 118
......
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