Splunk SPLK-1002 Practice Test - Questions Answers, Page 26

Extracted fields: Calculated fields are often based on fields that have already been extracted from your data. Extracted fields are those that Splunk has identified and pulled out from the event data based on patterns, delimiters, or other methods such as regular expressions or automatic extractions. These fields can then be used in expressions to create calculated fields. For example, you might have an extracted field for the time in seconds, and you want to create a calculated field for the time in minutes. You would use the extracted field in a calculation to create the new field. It's important to note that although fields generated within a search string (A) and regular expressions (C) can also be used in the calculation of a new field, and lookup tables (B) can be used to enrich data, option D is typically what one refers to when discussing calculated fields, as it implies a direct transformation or calculation based on fields that have been extracted from the raw data.

In Splunk, a variable for a macro is defined by placing the variable name inside dollar signs, like this: $variable name$. This syntax allows the macro to dynamically replace the variable with the appropriate value when the macro is invoked within a search. Using this method ensures that the search strings can be dynamically adjusted based on the variable's value at runtime.

Splunk Docs: Use macros

Splunk Answers: Defining and Using Macros

To filter for only IP addresses that appear more than five times in the search results for index=games, you can use a combination of the stats and where commands. The stats command counts the occurrences of each IP address and assigns the count to IP_count. The where command then filters the results to include only those IP addresses with a count greater than five.

Here is how the complete search would look:

index=games | stats count as IP_count by IP | where IP_count > 5

Splunk Docs: stats command

Splunk Docs: where command

Splunk Answers: Filtering results using stats and where commands

In Splunk, a field alias is used to create an alternative name for an existing field, making it easier to refer to data in a consistent manner across different searches and reports. Field aliases can be applied to both calculated fields and extracted fields. Calculated fields are those that are created using eval expressions, while extracted fields are typically those parsed from the raw data at index time or search time. This flexibility allows users to streamline their searches by using more intuitive field names without altering the underlying data. Field aliases cannot be applied to data in a lookup table, specific individual fields within a dataset, or directly to a host, source, or sourcetype.

In Splunk, a data model's root dataset is the foundational element upon which the rest of the data model is built. The root dataset can be of various types, including search, transaction, or event-based datasets. One of the key features of the root dataset is that it automatically inherits the knowledge objects associated with its base search. These knowledge objects include field extractions, lookups, aliases, and calculated fields that are defined for the base search, ensuring that the root dataset has all necessary contextual information from the outset. This allows users to build upon this dataset with additional child datasets and objects without having to redefine the base search's knowledge objects.

A POST workflow action in Splunk is designed to send data to an external web service by using HTTP POST requests. This type of workflow action can pass a combination of clear text strings and variables derived from the search results or event data. The clear text strings might include static text or predefined values, while the variables are dynamic elements that represent specific fields or values extracted from the Splunk events. This flexibility allows for constructing detailed and context-specific requests to external systems, enabling various integration and automation scenarios. The POST request can include both types of data, making it versatile for different use cases.

The Common Information Model (CIM) add-on in Splunk applies preconfigured data models to data at search time. This means that when a search is executed, the CIM add-on uses its predefined data models to normalize and map the relevant data to a common format. This approach ensures that data is interpreted and analyzed consistently across various datasets without modifying the data at index time.

Splunk Docs: About the Common Information Model

Splunk Answers: CIM Add-on Data Models

When using the Field Extractor (FX) in Splunk to perform field extraction, any consistent character can be used as a delimiter. The Field Extractor allows users to define how fields are separated in the raw event data, and as long as the delimiter is consistent, the FX tool can parse and extract the fields correctly.

Splunk Docs: Field Extractor

Splunk Answers: Field extraction delimiters

An event type in Splunk is defined by a search string that returns a specific set of events. The search string index=games sourcetype=score player=* score>9999 is valid because it filters events based on specific criteria directly within the main search command. This search will find all events in the games index with a sourcetype of score, where the player field exists, and the score is greater than 9999. This specificity and direct filtering make it suitable for defining an event type.

Splunk Docs: Create event types