Splunk SPLK-1002 Practice Test - Questions Answers, Page 25

An existing accelerated data model can be edited, but the data model must be de-accelerated before any structural edits can be made (Option C). This is because the acceleration process involves pre-computing and storing data, and changes to the data model's structure could invalidate or conflict with the pre-computed data. Once the data model is de-accelerated and edits are completed, it can be re-accelerated to optimize performance.

To group events by JSESSIONID, the correct search is index=web sourcetype=access_combined | transaction JSESSIONID | search SD470K92802F117 (Option B). The transaction command groups events that share the same JSESSIONID value, allowing for the analysis of all events associated with a specific session as a single transaction. The subsequent search for SD470K92802F117 filters these grouped transactions to include only those related to the specified session ID.

The transaction command is used instead of stats to group events based on start/end values (Option B). This is particularly useful in scenarios where related events span across multiple log entries and need to be analyzed as a single transaction, such as user sessions or multi-step transaction processes.

The CIM Add-on manual contains the descriptions of the data models that come with the Splunk Common Information Model (CIM) Add-on, as well as how to set up, use, and customize the add-on.

Reference

CIM Add-on manual

Splunk Common Information Model (CIM) | Splunkbase

Understand and use the Common Information Model Add-on - Splunk

Arguments are defined within the macro search string by using dollar signs on either side of the argument name, such as arg1 or fragment.

Reference

Search macro examples

Define search macros in Settings

Use search macros in searches

A Search workflow action is the appropriate choice when a user wants to retrieve a specific field value from an event and run a search in a new browser window within their Splunk instance (Option B). This type of workflow action allows users to define a search that utilizes field values from selected events as parameters, enabling more detailed investigation or context-specific analysis based on the original search results.

The regular expression mode of Field Extractor (FX) should be used for data with multiple, different characters separating fields or for unstructured data. The regular expression mode allows you to select a sample event and highlight the fields that you want to extract, and the field extractor generates a regular expression that matches similar events and extracts the fields from them. Reference SeeBuild field extractions with the field extractor - Splunk DocumentationandField Extractor: Select Method step - Splunk Documentation.

When using the Field Extractor (FX) tool in Splunk and the tool fails to extract a value from all appropriate events, there are specific steps you can take to improve the extraction process. These steps involve interacting with the FX tool and possibly adjusting the extraction method:

A) Select an additional sample event with the Field Extractor (FX) and highlight the missing value in the event. This approach allows Splunk to understand the pattern better by providing more examples. By highlighting the value in another event where it wasn't extracted, you help the FX tool to learn the variability in the data format or structure, improving the accuracy of the field extraction.

D) Edit the regular expression manually. Sometimes the FX tool might not generate the most accurate regular expression for the field extraction, especially when dealing with complex log formats or subtle nuances in the data. In such cases, manually editing the regular expression can significantly improve the extraction process. This involves understanding regular expression syntax and how Splunk extracts fields, allowing for a more tailored approach to field extraction that accounts for variations in the data that the automatic process might miss.

Options B and C are not typically related to improving field extraction within the Field Extractor tool. Re-ingesting data (B) does not directly impact the extraction process, and changing to a delimited extraction method (C) is not always applicable, as it depends on the specific data format and might not resolve the issue of missing values across events.

In Splunk, when using the chart command, the useother parameter can be set to false (f) to remove the 'OTHER' category, which is a bucket that Splunk uses to aggregate low-cardinality groups into a single group to simplify visualization. Here's how the options break down:

A) | chart count over CurrentStanding by Action useother=f This command correctly sets the useother parameter to false, which would prevent the 'OTHER' category from being displayed in the resulting visualization.

B) | chart count over CurrentStanding by Action usenull=f useother=t This command has useother set to true (t), which means the 'OTHER' category would still be included, so this is not a correct option.

C) | chart count over CurrentStanding by Action limit=10 useother=f Similar to option A, this command also sets useother to false, additionally imposing a limit to the top 10 results, which is a way to control the granularity of the chart but also to remove the 'OTHER' category.

D) | chart count over CurrentStanding by Action limit-10 This command has a syntax error (limit-10 should be limit=10) and does not include the useother=f clause. Therefore, it would not remove the 'OTHER' category, making it incorrect.

The correct answers to rewrite the syntax to remove the 'OTHER' category are options A and C, which explicitly set useother=f.