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Splunk SPLK-1004 Exam Syllabus Topics:

Section	Weight	Objectives
Dashboards, Forms, and Visualizations	20%	- Dashboard design best practices - Dynamic dashboards and forms 1. Tokens, inputs, dynamic drilldown, conditional rendering - Advanced visualizations 1. Custom visualizations, formatting, and layout
Search Optimization and Performance	15%	- Writing efficient SPL 1. Best practices, reducing search time, avoiding common mistakes - Using commands for optimization 1. tstats, highcharts, summary indexing
Lookups and Data Enrichment	15%	- Subsearches and advanced lookup use cases - Lookup management 1. Creating, editing, managing, and optimizing lookups - Lookup types 1. File-based, KV Store, external, geospatial lookups
Knowledge Objects	20%	- Data models and Pivot 1. Designing data models, using Pivot for analysis - Tags and event types - Fields and field extractions 1. Automatic, inline, and configured extractions; field aliases; calculated fields - Macros and workflow actions
Advanced Searching and Reporting	20%	- Result modification commands 1. sort, rename, replace, fields, dedup, head, tail - Statistical commands 1. stats, eventstats, streamstats, timechart - eval command and functions 1. Conversion, mathematical, string, date/time, conditional functions - Comparison and correlation 1. Comparing values, joins, transactions, correlation searches
Alerts and Monitoring	10%	- Alert management and logging - Alert configuration 1. Trigger conditions, scheduling, actions, throttling

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Splunk Core Certified Advanced Power User Sample Questions (Q108-Q113):

NEW QUESTION # 108
Which of the following is true about Log Event alerts?

A. They must be used with other alert actions.
B. They create new searchable events.
C. They require at least Power User role.
D. They cannot use tokens to reference event fields.

Answer: B

Explanation:
Log Event alerts in Splunk are designed to create new events in the index when specific conditions are met.
These events are then searchable like any other event, allowing for further analysis and correlation.
This functionality is particularly useful for tracking occurrences of specific conditions over time or triggering additional workflows based on the logged events.
Reference:Splunk Documentation on Alert Actions

NEW QUESTION # 109
Which search generates a field with a value of "hello"?

A. | makeresults field="hello"
B. | makeresults | fields="hello"
C. | makeresults | eval field=make{"hello"}
D. | makeresults | eval field="hello"

Answer: D

Explanation:
The correct search to generate a field with a value of"hello"is:
Copy
1
| makeresults | eval field="hello"
Here's why this works:
* makeresults: This command creates a single event with no fields.
* eval: Theevalcommand is used to create or modify fields. In this case, it creates a new field namedfield and assigns it the value"hello".
Example:
| makeresults
| eval field="hello"
This will produce a result like:
_time field
------------------- -----
<current_timestamp> hello
References:
* Splunk Documentation onmakeresults:https://docs.splunk.com/Documentation/Splunk/latest
/SearchReference/Makeresults
* Splunk Documentation oneval:https://docs.splunk.com/Documentation/Splunk/latest/SearchReference
/Eval

NEW QUESTION # 110
Which search generates a field with a value of "hello"?

A. | Makeresults | fields''hello''
B. | Makeresults | eval field-''hello''
C. | Makeresults | eval field =make{''hello''}
D. | Makeresults field-''hello''

Answer: B

Explanation:
To generate a field with a value of "hello" using the makeresults command in Splunk, the correct syntax is | makeresults | eval field="hello" (Option C). The makeresults command creates a single event, and the eval command is used to add a new field (named "field" in this case) with the specified value ("hello"). This is a common method for creating sample data or for demonstration purposes within Splunk searches.

NEW QUESTION # 111
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 values in the punch field.
C. The event data is broken up by major breakers and then broken up further by minor breakers.
D. The event data is broken up by a series of user-defined regex patterns.

Answer: C

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 # 112
Which of the following can be used to access external lookups?

A. Perl and binary executable
B. Python and Ruby
C. Python and binary executable
D. Perl and Python

Answer: C

Explanation:
Splunk supports the use of external lookups, which can be scripts or binary executables that enrich search results with external data. These external lookups can be written in various scripting languages or compiled as binary executables. Among the options given, Python and binary executables (Option D) are commonly used for creating external lookups in Splunk. Python is a widely used programming language that can easily interact with Splunk's API and data structures, and binary executables can be used for more complex or performance-critical lookup operations. Perl and Ruby (Options A and B) are less commonly used in this context, and Perl combined with binary executables (Option C) is not as standard for Splunk external lookups as Python.

NEW QUESTION # 113
......

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