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Splunk Core Certified Advanced Power User Sample Questions (Q92-Q97):

NEW QUESTION # 92
Repeating JSON data structures within one event will be extracted as what type of fields?

A. Mvindex
B. Single value
C. Lexicographical
D. Multivalue

Answer: D

Explanation:
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"]
}
References:
* Splunk Documentation on JSON Data Extraction:https://docs.splunk.com/Documentation/Splunk/latest
/Data/ExtractfieldsfromJSON
* Splunk Documentation on Multivalue Fields:https://docs.splunk.com/Documentation/Splunk/latest
/SearchReference/MultivalueEvalFunctions

NEW QUESTION # 93
Which of these generates a summary index containing a count of events byproduct_id?

A. stats count by product_id
B. sistats count by product_id
C. stats si(product_id)
D. sistats summary index by product_id

Answer: B

Explanation:
The correct command to generate a summary index containing a count of events by product_id is:
sistats count by product_id
Here's why this works:
* sistats: This command is specifically designed for creating summary indexes. It pre-aggregates data and stores it in a format optimized for fast retrieval.
* count by product_id: This part of the command calculates the count of events grouped by the product_idfield.
Summary indexing is useful when you want to store pre-aggregated data for faster reporting. For example, instead of querying raw data every time, you can query the summary index to get quick results.
Other options explained:
* Option A: Incorrect becausestats si(product_id)is invalid syntax.
* Option B: Incorrect becausestatsis used for real-time aggregation but does not create summary indexes.
* Option D: Incorrect becausesistats summary index by product_idis invalid syntax.
Example:
index=main | sistats count by product_id
References:
* Splunk Documentation onsistats:https://docs.splunk.com/Documentation/Splunk/latest/SearchReference
/sistats
* Splunk Documentation on Summary Indexing:https://docs.splunk.com/Documentation/Splunk/latest
/Knowledge/Usesummaryindexing

NEW QUESTION # 94
What function can be used as an alternative to coalesce to return the first value from a list of fields that is not null?

A. exact
B. mvzip
C. bin
D. case

Answer: D

Explanation:
Comprehensive and Detailed Step by Step Explanation:
The case function can be used as an alternative to coalesce to return the first non-null value. While coalesce (field1, field2, field3) will return the first non-null value, case(condition1, value1, condition2, value2, ...) allows more flexibility by evaluating conditions.
Reference:Splunk Documentation - case Function

NEW QUESTION # 95
What is a performance improvement technique unique to dashboards?

A. Using global searches
B. Using stats instead of transaction
C. Using data model acceleration
D. Using report acceleration

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 acceleration:Data 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 # 96
Assuming a standard time zone across the environment, what syntax will always return ewnts from between
2:00am and 5:00am?

A. earliest=2h@ AND latest=5h3h
B. time_hour>-2 AND time_hour>-5
C. earliest=-2h@h AND latest=-5h@h
D. datehour>-2 AND date_hour<5

Answer: C

Explanation:
To always return events from between 2:00 AM and 5:00 AM, assuming a standard time zone across the environment, the correct Splunk search syntax is earliest=-2h@h AND latest=-5h@h (Option B). This syntax uses relative time modifiers to specify a range starting 2 hours ago from the current hour (-2h@h) and ending
5 hours ago from the current hour (-5h@h), effectively capturing the desired time window.

NEW QUESTION # 97
......

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Eli Martin Eli Martin

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