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SPLK-1004 Free Dumps, SPLK-1004 Latest Exam Answers

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Earning the SPLK-1004 certification is a great way to showcase your expertise in Splunk and demonstrate your ability to use advanced features to solve complex problems. It is also a valuable asset for those looking to advance their career in the field of data analytics. With this certification, you can demonstrate to potential employers and clients that you have advanced knowledge and skills in Splunk, making you a highly valuable asset to any organization.

The Splunk SPLK-1004 Exam focuses on testing candidates' ability to work with advanced search, reporting, alerting, and dashboarding techniques in Splunk Core. SPLK-1004 exam is designed to measure the candidate's proficiency in analyzing complex data sets and creating advanced reports and visualizations to drive insights and decision-making.

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Splunk Core Certified Advanced Power User Sample Questions (Q79-Q84):

NEW QUESTION # 79
What XML element is used to pass multiple fields into another dashboard using a dynamic drilldown?

A. <link field_"sources_field_name">
B. <pas_token field_"sources_field_name">
C. <condition field_"sources_Field_name">
D. <drilldown field_"sources_Field_name">

Answer: A

Explanation:
In Splunk Simple XML for dashboards, dynamic drilldowns are configured within the<drilldown>element, not<link>,<condition>, or<pass_token>. To pass multiple fields to another dashboard, you would use a combination of<set>tokens within the<drilldown>element. Each<set>token specifies a field or value to be passed. The correct configuration might look something like this within the<drilldown>element:
<drilldown>
<set token="token1">$row.field1$</set>
<set token="token2">$row.field2$</set>
<link target="_blank">/app/search/new_dashboard</link>
</drilldown>
In this configuration,$row.field1$and$row.field2$are placeholders for the field values from the clicked event, which are assigned to tokenstoken1andtoken2. These tokens can then be used in the target dashboard to receive the values. The<link>element specifiesthe target dashboard. Note that the exact syntax can vary based on the specific requirements of the drilldown and the dashboard configuration.

NEW QUESTION # 80
Which commands should be used in place of a subsearch if possible?

A. mvexpand and/or where
B. bin and/or where
C. stats and/or eval
D. untable and/or xyseries

Answer: C

Explanation:
stats and eval are recommended over subsearches because they are more efficient and scalable. Subsearches can be slow and resource-intensive, whereas stats aggregates data, and eval performs calculations within the search.
The stats and eval commands should be used instead of subsearches whenever possible because subsearches have performance limitations. They return only a maximum of 10,000 results or execute within 60 seconds by default, which may cause incomplete results. Using stats allows aggregation of large datasets efficiently, while eval can manipulate field values within a search rather than relying on subsearches.
Reference:
Splunk Documentation - Stats Command
Splunk Documentation - Eval Command

NEW QUESTION # 81
Which of the following statements is accurate regarding the append command?

A. It is used with a subsearch and oily accesses historical data.
B. It cannot be used with a subsearch and only accesses real-time searches.
C. It cannot be used with a subsearch and only accesses historical data.
D. It is used with a subsearch and only accesses real-lime searches.

Answer: A

Explanation:
The append command in Splunk is often used with a subsearch to add additional data to the end of the primary search results, and it can access historical data (Option B). This capability is useful for combining datasets from different time ranges or sources, enriching the primary search results with supplementary information.

NEW QUESTION # 82
Which of these generates a summary index containing a count of events byproduct_id?

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

Answer: A

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 # 83
Why use the tstats command?

A. To generate statistics on indexed fields.
B. As an alternative to the summary command.
C. To generate statistics on search-time fields.
D. To generate an accelerated data model.

Answer: A

Explanation:
The tstats command is used to generate statistics on indexed fields, particularly from accelerated data models.
It operates on indexed-time summaries, making it more efficient than using raw data.
Thetstatscommand is used togenerate statistics on indexed fields. It is highly efficient because it operates directly on indexed data (e.g., metadata or data model datasets) rather than raw event data.
Here's why this works:
* Indexed Fields: Indexed fields include metadata fields like_time,host,source, andsourcetype, as well as fields defined in data models. Since these fields are preprocessed and stored in the index, querying them withtstatsis faster than searching raw events.
* Performance:tstatsis optimized for large-scale searches and is particularly useful for summarizing data across multiple indexes or time ranges.
* Data Models:tstatscan also query data model datasets, making it a powerful tool for working with accelerated data models.

NEW QUESTION # 84
......

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