Splunk SPLK-1002 Exam Dumps

Data models are collections of datasets that represent your data in a structured and hierarchical way. Data models define how your data is organized into objects and fields. Data models can be composed of one or more of the following datasets:

Events datasets: These are the base datasets that represent raw events in Splunk. Events datasets can be filtered by constraints, such as search terms, sourcetypes, indexes, etc.

Search datasets: These are derived datasets that represent the results of a search on events or other datasets. Search datasets can use any search command, such as stats, eval, rex, etc., to transform the data.

Transaction datasets: These are derived datasets that represent groups of events that are related by fields, time, or both. Transaction datasets can use the transaction command or event types with transactiontype=true to create transactions.

: A comparison operator is a symbol that compares two values and returns a Boolean result (true or false)2.Splunk supports various comparison operators such as <, >, =, !=, <=, >=, IN and LIKE2.However, ?= is not a valid comparison operator in Splunk and will cause a syntax error if used in a search string2. Therefore, option E is correct, while options A, B, C and D are incorrect because they are valid comparison operators in Splunk

In Splunk, macros that accept arguments are defined with placeholders for those arguments in the format example(var1, var2). In the search example(100,200), '100' and '200' are the values passed for var1 and var2 respectively.

Splunk Docs -- Macros

The regular expression mode of Field Extractor (FX) should be used for data with multiple, different characters separating fields or for unstructured dat

a. 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.

The relationship between data models and pivots is that data models provide the datasets for pivots. Data models are collections of datasets that represent your data in a structured and hierarchical way. Data models define how your data is organized into objects and fields. Pivots are user interfaces that allow you to create data visualizations that present different aspects of a data model. Pivots let you select options from menus and forms to create charts, tables, maps, etc., without writing any SPL code. Pivots use datasets from data models as their source of data. Pivots and data models are not the same thing, as pivots are tools for visualizing data models. Pivots do not provide datasets for data models, but rather use them as inputs.

Therefore, only statement A is true about the relationship between data models and pivots.