Vce SPLK-1002 Questions Latest

Auto-Extracted fields in Splunk Data Models are derived directly from the indexed data based on the existing fields within the events. These fields are identified and extracted by Splunk automatically, without the need for explicit field extractions configured by the user. Understanding the characteristics of Auto-Extracted fields is crucial for effectively managing Data Models and utilizing them in Pivot tables for analysis.

A. Auto-Extracted fields can be hidden in Pivot. This is true. When building a Data Model, you have the option to hide certain fields from appearing in Pivot, making the Pivot table cleaner and more focused on the fields that are most relevant for analysis. This helps in reducing clutter and focusing on the data that matters most to the users.

B. Auto-Extracted fields can have their data type changed. This statement is not typically accurate for Auto-Extracted fields. The data type of an Auto-Extracted field is determined by Splunk based on the field's content in the indexed data. While you can assign a type to a field when you manually create a field in a data model, the inherent data type of Auto-Extracted fields is not something that is changed within the Data Model itself.

C. Auto-Extracted fields can be given a friendly name for use in Pivot. This is correct. Within Data Models, you can assign a more user-friendly, descriptive name to an Auto-Extracted field. This feature is particularly useful in making Data Models more intuitive and easier to use for those who may not be familiar with the original field names or when the original field names are not descriptive or user-friendly.

D. Auto-Extracted fields can be added if they already exist in the dataset with constraints. This is true. Auto-Extracted fields are based on fields that already exist in the data. When you define a dataset within a Data Model, you can apply constraints to narrow down the events that the dataset includes. The Auto-Extracted fields are then identified from this constrained dataset. This means that the fields must already be present in the data that meets the dataset's constraints to be available for auto-extraction.

In summary, Auto-Extracted fields in Splunk Data Models offer a flexible and efficient way to utilize existing data fields within Pivot tables, with options to rename them for clarity and hide unnecessary fields to streamline data analysis.

A macro is a way to save a commonly used search string as a variable that you can reuse in other searches1. When you create a macro, you can define arguments that are placeholders for values that you specify at execution time1. The argument values are used to resolve the search string when the macro is invoked, not when it is created1. Therefore, statements B and C are true, while statements A and D are false.

The Splunk Common Information Model (CIM) add-on is a Splunk app that contains data models to help you normalize data from different sources and formats. The CIM add-on defines a common and consistent way of naming and categorizing fields and events in Splunk. This makes it easier to correlate and analyze data across different domains, such as network, security, web, etc. The CIM add-on does not use machine learning to normalize data, but rather relies on predefined field names and values. The CIM add-on does not contain dashboards that show how to map data, but rather provides documentation and examples on how to use the data models. The CIM add-on is not automatically installed in a Splunk environment, but rather needs to be downloaded and installed from Splunkbase.