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| Section | Weight | Objectives |
|---|---|---|
| Exploring Field Extractions | 10% | - Using the Field Extractor - Using calculated fields - Using field aliases - Creating custom fields |
| Exploring Lookups | 4% | - Applying advanced lookup options - Using geospatial lookups - Using KV Store lookups - Understanding best practices for lookups - Including and excluding events based on lookup values - Using external lookups |
| Exploring Search Optimization | 10% | - Using tsidx files - Using report acceleration - Using search optimization techniques - Using summary indexing |
| Exploring Alerts | 4% | - Logging and indexing searchable alert events - Using alert manager - Referencing alert actions - Understanding alert actions |
| Exploring Data Models | 10% | - Using pivot - Understanding data models - Creating data models - Using data model objects |
| Exploring Dashboards and Forms | 15% | - Using event handlers - Using dynamic form inputs - Creating dashboards using Simple XML - Using tokens - Using drilldowns |
| Exploring Statistical Commands | 4% | - Performing statistical analysis with stats function - Using fieldsummary - Using appendpipe - Using count and list functions - Using eventstats - Using streamstats |
| Exploring Splunk's Search Processing Language | 15% | - Using advanced search commands - Using tags and event types - Using workflow actions - Using search macros - Using transactions |
| Exploring eval Command Functions | 4% | - Using informational functions - Using text functions - Using statistical functions - Using comparison and conditional functions - Using makeresults command - Using conversion functions |
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NEW QUESTION # 20
Which of the following is accurate about cascading inputs?
Answer: B
Explanation:
Cascading inputs allow one input's selection to determine the options available in subsequent inputs. An event handler can reset the cascading sequence based on user interactions, ensuring the following inputs reflect appropriate options based on prior selections.
NEW QUESTION # 21
Which of the following best describes the process for tokenizing event data?
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 # 22
Which of the following functions' primary purpose is to convert epoch time to a string format?
Answer: C
Explanation:
The strftime function in Splunk is used to convert epoch time (also known as POSIX time or Unix time, which is a system for describing points in time as the number of seconds elapsed since January 1, 1970) into a human-readable string format. This function is particularly useful when formatting timestamps in search results or when creating more readable time representations in dashboards and reports. The strftime function takes an epoch time value and a format string asarguments and returns the formatted time as a string according to the specified format. The other options (tostring, strptime, and tonumber) serve different purposes: tostring converts values to strings, strptime converts string representations of time into epoch format, and tonumber converts values to numbers.
NEW QUESTION # 23
Which syntax is used when referencing multiple CSS files in a view?
Answer: D
Explanation:
When referencing multiple CSS files in a Splunk dashboard view (within Simple XML), the correct approach is to include separate stylesheet attributes for each CSS file. The syntax for this would be similar to
<dashboard stylesheet="custom.css" stylesheet="userapps.css"> (Option C). This method allows the dashboard to load and apply the styles from both CSS files, enhancing the dashboard's visual appearance and user interface design.
NEW QUESTION # 24
A report named "Linux logins" populates a summary index with the search string sourcetype=linux_secure | sitop src_ip user. Which of the following correctly searches against the summary index for this data?
Answer: C
Explanation:
The correct way to search against the summary index for this data is:
index=summary search_name="Linux logins" | stats count by src_ip user
Here's why this works:
* Summary Index: Summary indexes store pre-aggregated data generated by scheduled reports or saved searches. To query this data, you must specify theindex=summaryand filter by thesearch_namefield, which identifies the specific report that populated the summary index.
* Aggregation: The original search usedsitop, which is designed for summary indexing. When querying the summary index, you should usestatsto aggregate the pre-aggregated data further.
Example:
index=summary search_name="Linux logins"
| stats count by src_ip user
References:
Splunk Documentation on Summary Indexing:https://docs.splunk.com/Documentation/Splunk/latest
/Knowledge/Usesummaryindexing
Splunk Documentation onsitop:https://docs.splunk.com/Documentation/Splunk/latest/SearchReference/sitop
NEW QUESTION # 25
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