Pass the Splunk Splunk Core Certified Power User SPLK-1002 Questions and answers with CertsForce

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

References

Search macro examples

Define search macros in Settings

Use search macros in searches

The correct answer is B. Delimiters. This is because the delimiters method is designed for structured event data, such as data from files with headers, where all of the fields in the events are separated by a common delimiter, such as a comma or space. You can select a sample event, identify the delimiter, and then rename the fields that the field extractor finds. You can learn more about the delimiters method from the Splunk documentation1. The other options are incorrect because they are not suitable for comma-separated data. The regular expression method works best with unstructured event data, where you select and highlight one or more fields to extract from a sample event, and the field extractor generates a regular expression that matches similar events and extracts the fields from them. The eval expression is a command that lets you calculate new fields or modify existing fields using arithmetic, string, and logical operations. The table extraction is a feature that lets you extract tabular data from PDF files or web pages. You can learn more about these methods from the Splunk documentation23 .

The search string below returns events from the data model named Application_State.

| datamodel Application_State All_Application_State search

The search string does the following:

It uses the datamodel command to access a data model in Splunk. The datamodel command takes two arguments: the name of the data model and the name of the dataset within the data model.

It specifies the name of the data model as Application_State. This is a predefined data model in Splunk that contains information about web applications.

It specifies the name of the dataset as All_Application_State. This is a root dataset in the data model that contains all events from all child datasets.

It uses the search command to filter and transform the events from the dataset. The search command can use any search criteria or command to modify the results.

Therefore, the search string returns events from the data model named Application_State.

Data model attributes are fields that are added to a dataset from search time field extractions, calculated fields, lookups, or aliases. They can be created within the data model editor or inherited from a parent dataset. They can be edited or removed unless they are required by the data model. They can be added to any type of dataset, not just root search datasets.ReferencesSee About data models, [Define data model attributes], and [Edit data model datasets] in the Splunk Documentation.

The Splunk Common Information Model (CIM) Add-on includes a variety of data models designed to normalize data from different sources to allow for cross-source reporting and analysis. Among the data models included, Alerts (Option B) and Email (Option D) are part of the CIM. The Alerts data model is used for data related to alerts and incidents, while the Email data model is used for data pertaining to email messages and transactions. User permissions (Option A) and Databases (Option C) are not data models included in the CIM; rather, they pertain to aspects of data access control and specific types of data sources, respectively, which are outside the scope of the CIM's predefined data models.

The type of workflow action that sends field values to an external resource (e.g. a ticketing system) is POST. A POST workflow action allows you to send a POST request to a URI location with field values or static values as arguments. For example, you can use a POST workflow action to create a ticket in an external system with information from an event.

 The following commands are used when creating visualizations: geom, geostats, and iplocation. Visualizations are graphical representations of data that show trends, patterns, or comparisons. Visualizations can have different types, such as charts, tables, maps, etc. Visualizations can be created by using various commands that transform the data into a suitable format for the visualization type. Some of the commands that are used when creating visualizations are:

geom: This command is used to create choropleth maps that show geographic regions with different colors based on some metric. The geom command takes a KMZ file as an argument that defines the geographic regions and their boundaries. The geom command also takes a field name as an argument that specifies the metric to use for coloring the regions.

geostats: This command is used to create cluster maps that show groups of events with different sizes and colors based on some metric. The geostats command takes a latitude and longitude field as arguments that specify the location of the events. The geostats command also takes a statistical function as an argument that specifies the metric to use for sizing and coloring the clusters.

iplocation: This command is used to create location-based visualizations that show events with different attributes based on their IP addresses. The iplocation command takes an IP address field as an argument and adds some additional fields to the events, such as Country, City, Latitude, Longitude, etc. The iplocation command can be used with other commands such as geom or geostats to create maps based on IP addresses.