Splunk SPLK-1002 Practice Test - Questions Answers, Page 3

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.

Event types are custom categories of events that are based on search criteria. Event types can be used to label events with meaningful names, such as error, success, login, logout, etc. Event types can also be used to create transactions, alerts, reports, dashboards, etc. Event types can be created in two ways:

By going to the Settings menu and clicking Event Types > New. This will open a form where you can enter the name, description, search string, app context, and tags for the event type.

By selecting an event in search results and clicking Event Actions > Build Event Type. This will open a dialog box where you can enter the name and description for the event type. The search string will be automatically populated based on the selected event.

Event types cannot be created by using the searchtypes command in the search bar, as this command does not exist in Splunk. Event types can also be created by editing the event_type stanza in the transforms.conf file, not the props.conf file.

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.

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.

A root event dataset is the base dataset for a data model that defines the source or sources of the data and the constraints and fields that apply to the data1.A root event dataset has two parts: constraints and fields1.Constraints are filters that limit the data to a specific index, source, sourcetype, host or search string1.Fields are the attributes that describe the data and can be extracted, calculated or looked up1. Therefore, option C is correct, while options A, B and D are incorrect.

An event type is a way to categorize events based on a search string that matches the events2.You can use event types to simplify your searches by replacing long or complex search strings with short and simple event type names2.An event type is more effective than a saved search when the search string needs to be used in future searches because it allows you to reuse the search string without having to remember or type it again2. Therefore, option C is correct, while options A, B and D are incorrect because they are not scenarios where an event type is more effective than a saved search.

A chart is a graphical representation of your search results that shows the relationship between two or more fields2.You can display a chart in stack mode by changing the Stack Mode option in the Format menu2.Stack mode allows you to stack multiple series on top of each other in a chart to show the cumulative values of each series2. Therefore, option C is correct, while options A, B and D are incorrect because they are not ways to display a chart in stack mode.

As mentioned before, an event type is a way to categorize events based on a search string that matches the events2.Event types can be tagged, which means that you can apply descriptive labels to event types and use them in your searches2. Therefore, option A is correct.Event types categorize events based on a search string, which means that you can define an event type by specifying a search string that matches the events you want to include in the event type2. Therefore, option C is correct.Event types can be a useful method for capturing and sharing knowledge, which means that you can use event types to organize your data into meaningful categories and share them with other users in your organization2. Therefore, option D is correct.Event types do not have to include a time range, which means that you can create an event type without specifying a time range for the events2. Therefore, option B is incorrect.

Knowledge objects are entities that you create to add knowledge to your data and make it easier to search and analyze2.Some examples of knowledge objects are field extractions, field aliases and lookups2.Field extractions are methods that extract fields from your raw data using various techniques such as regular expressions, delimiters or key-value pairs2.Field aliases are ways to assign alternative names to existing fields without changing the original field names or values2.Lookups are ways to enrich your data with additional information from external sources such as CSV files or databases2.The order in which these knowledge objects/configurations are applied is as follows: field extractions, field aliases and then lookups2.This means that Splunk first extracts fields from your raw data, then applies any aliases to the extracted fields and then performs any lookups on the aliased fields2. Therefore, option B is correct, while options A, C and D are incorrect.