Explanation: The recommended approach when using the Splunk Common Information Model (CIM) add-on to normalize data is A. Consult the CIM data model reference tables. This is because the CIM data model reference tables provide detailed information about the fields and tags that are expected for each dataset in a data model. By consulting the reference tables, you can determine which data models are relevant for your data source and how to map your data fields to the CIM fields. You can also use the reference tables to validate your data and troubleshoot any issues with normalization. You can find the CIM data model reference tables in the Splunk documentation1or in the Data Model Editor page in Splunk Web2. The other options are incorrect because they are not related to the CIM add-on or data normalization. The authentication command is a custom command that validates events against the Authentication data model, but it does not help you to normalize other types of data. The correlation command is a search command that performs statistical analysis on event fields, but it does not help you to map your data fields to the CIM fields. The CIM event type reference tables do not exist, as event types are not part of the CIM add-on.

Explanation:
An existing accelerated data model can be edited, but the data model must be deaccelerated 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.

Explanation: Tags are a type of knowledge object that enable you to assign descriptive keywords to events based on the values of their fields. Tags can help you to search more efficiently for groups of event data that share common characteristics, such as functionality, location, priority, etc. For example, you can tag all the IP addresses of your routers as router, and then search for tag=router to find all the events related to your routers. Tags can also help you to normalize data from different sources by using the same tag name for equivalent field values. For example, you can tag the field values error, fail, and critical as severity=high, and then search for severity=high to find all the events with high severity level.

Information needed to create a GET workflow action includes the following: a name of the workflow action, a URI where the user will be directed at search time, and a label that will appear in the Event Action menu at search time. A GET workflow action is a type of workflow action that performs a GET request when you click on a field value in your search results. A GET workflow action can be configured with various options, such as:
A name of the workflow action: This is a unique identifier for the workflow action that is used internally by Splunk. The name should be descriptive and meaningful for the purpose of the workflow action.
A URI where the user will be directed at search time: This is the base URL of the external web service or application that will receive the GET request. The URI can include field value variables that will be replaced by the actual field values at search time. For example, if you have a field value variable ip, you can write it as http://example.com/ip=$ip to send the IP address as a parameter to the external web service or application.
A label that will appear in the Event Action menu at search time: This is the display name of the workflow action that will be shown in the Event Action menu when you click on a field value in your search results. The label should be clear and concise for the user to understand what the workflow action does.
Therefore, options A, B, and C are correct.

Explanation: The search modes determine how Splunk processes your search and displays your results2. There are three search modes: Fast, Smart and Verbose2. The search mode that automatically returns all extracted fields in the fields sidebar is Verbose2. The Verbose mode shows all the fields that are extracted from your events, including default fields, indexed fields and search-time extracted fields2. The fields sidebar is a panel that shows the fields that are present in your search results2. Therefore, option C is correct, while options A and B are incorrect because they are not search modes that automatically return all extracted fields in the fields sidebar.

Explanation: The correct answer is B. Root event dataset. This is because root event datasets are defined by a constraint that filters out events that are not relevant to the dataset. A constraint for a root event dataset is a simple search that returns a fairly wide range of data, such as sourcetype=access_combined. Without a constraint, a root event dataset would include all the events in the index, which is not useful for data modeling. You can learn more about how to design data models and add root event datasets from the Splunk documentation1. The other options are incorrect because root transaction datasets and root search datasets have different ways of defining their datasets, such as transaction definitions or complex searches, and root child datasets are not a valid type of root dataset.

Explanation: The transaction and stats commands are two ways to group events from one or more data sources based on common fields or time ranges. The transaction command creates a single event out of a group of related events, while the stats command calculates summary statistics over a group of events. The eval and coalesce commands are used to create or combine fields, not to group events. The format command is used to format the results of a subsearch, not to group events. The top and rare commands are used to rank the most or least common values of a field, not to group events.

Explanation: During the validation step of the Field Extractor workflow, you can remove values that aren’t a match for the field you want to define2. The validation step allows you to review and edit the values that have been extracted by the FX and make sure they are correct and consistent2. You can remove values thataren’t a match by clicking on them and selecting Remove Value from the menu2. This will excludethem from your field extraction and update the regular expression accordingly2. Therefore, option A is correct, while options B and C are incorrect because they are not actions that you can perform during the validation step of the Field Extractor workflow.

Explanation: 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 datamodel 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.

Explanation: "Calculated fields can reference all types of field extractions and field aliasing, but they cannot reference lookups, event types, or tags."

Explanation: This search string is not valid: index=web status=50* | chart count over host,status2. This search string uses an invalid syntax for the chart command. The chart command requires one field after the over clause and optionally one field after the by clause. However, this search string has two fields after the over clause separated by a comma. This will cause a syntax error and prevent the search from running. Therefore, option A is correct, while options B and C are incorrect because they are valid search strings that use the chart command correctly.