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[General] SPLK-1004 New APP Simulations & SPLK-1004 Reasonable Exam Price

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【General】 SPLK-1004 New APP Simulations & SPLK-1004 Reasonable Exam Price

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To prepare for the SPLK-1004 certification exam, candidates should have a strong foundation in Splunk Core concepts and should be comfortable working with large volumes of data. In addition to Splunk Core proficiency, exam takers should also have experience with data modeling, advanced searches, and visualizations in Splunk. The SPLK-1004 exam is a proctored, timed exam that tests a candidate's knowledge and skills in a variety of areas, including searching and reporting, creating dashboards and visualizations, using data models, and managing knowledge objects. Passing SPLK-1004 exam is a valuable credential for Splunk professionals who want to demonstrate their expertise and advance their careers in the field.
To be eligible for the SPLK-1004 Exam, candidates must first pass the Splunk Core Certified User exam, which tests basic knowledge of Splunk search, indexers, and forwarders. The advanced power user exam builds on this foundation and covers topics such as building complex queries using search commands, creating advanced visualizations with Splunk dashboards, and using Splunk's alerting and reporting features. SPLK-1004 exam is designed to challenge even the most experienced Splunk users, making it a valuable credential for those seeking to advance their careers in the field of data analysis and management.
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If you are looking to demonstrate your advanced knowledge of Splunk and increase your job prospects, the Splunk Core Certified Advanced Power User (SPLK-1004) certification is an excellent certification to pursue. SPLK-1004 exam covers advanced Splunk concepts, and candidates must score at least 70% to pass the exam and earn the certification. With the right preparation and study materials, you can pass the SPLK-1004 Exam and become a certified Splunk expert.
Splunk Core Certified Advanced Power User Sample Questions (Q121-Q126):NEW QUESTION # 121
What is a performance improvement technique unique to dashboards?
  • A. Using global searches
  • B. Using report acceleration
  • C. Using data model acceleration
  • D. Using stats instead of transaction
Answer: A
Explanation:
In Splunk, dashboards are powerful tools for visualizing and analyzing data. However, as dashboards grow in complexity and the volume of data increases, performance optimization becomes critical. One technique unique to dashboards is the use ofglobal searches.
What Are Global Searches?
A global search allows multiple panels within a dashboard to share the same base search. Instead of each panel running its own independent search, all panels derive their results from a single, shared search. This reduces the computational load on the Splunk instance because it eliminates redundant searches and ensures that the data is processed only once.
Why Is This Unique to Dashboards?
Global searches are specifically designed for dashboards where multiple panels often rely on the same dataset or search logic. By consolidating the search into one query, Splunk avoids duplicating effort, which improves performance significantly. This technique is not applicable to standalone searches or reports, making it unique to dashboards.
Comparison with Other Options:
* B. Using data model accelerationata model acceleration (DMA) is a powerful feature for speeding up searches over large datasets by precomputing and storing summarized data. However, it is not unique to dashboards-it can be used in any type of search or report.
* C. Using stats instead of transaction:Replacingtransactioncommands withstatsis a general best practice for improving search performance. While this is a valid optimization technique, it applies universally across Splunk and is not specific to dashboards.
* D. Using report acceleration:Report acceleration is another general-purpose optimization technique that speeds up saved searches by creating summaries of the data. Like DMA, it is not exclusive to dashboards.
Benefits of Global Searches:
* Reduced Search Load:By sharing a single search across multiple panels, the number of searches executed is minimized.
* Faster Dashboard Loading:Since the data is fetched once and reused, dashboards load faster.
* Consistent Results:All panels using the global search will display consistent results derived from the same dataset.
Example of Global Search in a Dashboard:
<dashboard>
<search id="base_search">
<query>index=main sourcetype=access_combined | fields clientip, status, method</query>
</search>
<panel>
<title>Status Codes</title>
<table>
<search base="base_search">
<query>| stats count by status</query>
</search>
</table>
</panel>
<panel>
<title>Top Clients</title>
<chart>
<search base="base_search">
<query>| top clientip</query>
</search>
</chart>
</panel>
</dashboard>
In this example, thebase_searchis defined once and reused by both panels. Each panel adds additional processing (statsortop) to the shared results, reducing redundancy.
References:
Splunk Documentation - Dashboard Best Practices:https://docs.splunk.com/Documentation/Splunk/latest
/Viz/BestPracticesThis document highlights the importance of global searches for optimizing dashboard performance.
Splunk Documentation - Global Searches:https://docs.splunk.com/Documentation/Splunk/latest/Viz
/PanelreferenceforSimplifiedXML#Global_searchesDetailed explanation of how global searches work and their implementation in dashboards.
Splunk Core Certified Power User Learning Path:The official Splunk training materials emphasize the use of global searches as a key technique for improving dashboard performance.
By leveraging global searches, users can ensure their dashboards remain efficient and responsive even as data volumes grow. This makesOption Athe correct and verified answer.

NEW QUESTION # 122
Which of the following is true when comparing the rex and erex commands?
  • A. The rex command is similar to automatic field extraction while erex isn't
  • B. The erex command uses data samples to generate regular expressions while rex doesn't
  • C. The rex command requires knowledge of regular expressions while erex doesn't
  • D. The erex command requires knowledge of regular expressions while rex doesn't
Answer: C
Explanation:
The rex and erex commands in Splunk are both used for field extraction, but they differ in their approach and requirements.
According to Splunk Documentation:
"rex: Specify a Perl regular expression named groups to extract fields while you search."
"erex: Use the erex command to extract data from a field when you do not know the regular expression to use.
The command automatically extracts field values that are similar to the example values you specify." This indicates that:
* The rex command requires users to have knowledge of regular expressions to define the extraction patterns.
* The erex command is designed for users who may not be familiar with regular expressions, allowing them to provide example values, and Splunk generates the appropriate regular expression.
Reference:erex - Splunk Documentation

NEW QUESTION # 123
Which statement about the coalesce function is accurate?
  • A. It can take only a single argument.
  • B. It can return null or non-null values.
  • C. It can take a maximum of two arguments.
  • D. It can be used to create a new field in the results set.
Answer: D
Explanation:
The coalesce function in Splunk is used to evaluate each argument in order and return the first non-null value.
This function can be used within an eval expression to create a new field in the results set, which will contain the first non-null value from the list of fields provided as arguments to coalesce. This makes it particularly useful in situations where data may be missing or inconsistently populated across multiple fields, as it allows for a fallback mechanism to ensure that some value is always presented.

NEW QUESTION # 124
Which commands can run on both search heads and indexers?
  • A. Distributable streaming commands
  • B. Transforming commands
  • C. Centralized streaming commands
  • D. Dataset processing commands
Answer: A
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.

NEW QUESTION # 125
Which of the following is not a common default time field?
  • A. date minute
  • B. date_day
  • C. date_year
  • D. date_zone
Answer: D
Explanation:
In Splunk, common default time fields include date_minute, date_year, and date_day, which represent the minute, year, and day parts of event timestamps, respectively. date_zone (Option A) is not recognized as a common default time field in Splunk. The platform typically uses fields like _time and various date_* fields for time-related information but does not use date_zone as a standard time field.

NEW QUESTION # 126
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