SPLK-1002 Exam Dumps

Explanation: Child datasets can be added to root datasets to narrow down the search. Datasets are collections of events that represent your data in a structured and hierarchical way. Datasets can be created by using commands such as datamodel or pivot. Datasets can have different types, such as events, search, transaction, etc. Datasets can also have different levels, such as root or child. Root datasets are base datasets that contain all events from a data model or an index. Child datasets are derived datasets that contain a subset of events from a parent dataset based on some constraints, such as search terms, fields, time range, etc. Child datasets can be added to root datasets to narrow down the search and filter out irrelevant events.

Explanation: The following statements about tags are true: tags are based on field/value pairs and tags categorize events based on a search. Tags are custom labels that can be applied to fields or field values to provide additional context or meaning for your data. Tags can be used to filter or analyze your data based on common concepts or themes. Tags can be created by using various methods, such as search commands, configuration files, user interfaces, etc. Some of the characteristics of tags are:
Tags are based on field/value pairs: This means that tags are associated with a specific field name and a specific field value. For example, you can create a tag called “alert” for the field name “status” and the field value “critical”. This means that only events that have status=critical will have the “alert” tag applied to them.
Tags categorize events based on a search: This means that tags are defined by a search string that matches the events that you want to tag. For example, you can create a tag called “web” for the search string sourcetype=access_combined. This means that only events that match the search string sourcetype=access_combined will have the “web” tag applied to them.
The following statements about tags are false: tags are case-insensitive and tags are designed to make data more understandable. Tags are case-sensitive and tags are designed to make data more searchable. Tags are case-sensitive: This means that tags must match the exact case of the field name and field value that they are associated with.
For example, if you create a tag called “alert” for the field name “status” and the field value “critical”, it will not apply to events that have status=CRITICAL or Status=critical. Tags are designed to make data more searchable: This means that tags can help you find relevant events or patterns in your data by using common concepts or themes. For example, if you create a tag called “web” for the search string sourcetype=access_combined, you can use tag=web to find all events related to web activity.

Explanation: When you define a calculated field, you can specify the name of the field that the eval expression will create or modify. If the name of the calculated field matches the name of an existing extracted field, the calculated field will override the extracted field and replace its value with the result of the eval expression. This means that the original value of the extracted field will not be available for searching or analysis. To avoid this, you should use a unique name for your calculated field or use a different name for your extracted field2. 1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, Configure calculated fields with props.conf.

Explanation: The correct answer is D. limit=0. This is because the limit argument specifies the maximum number of series to display in the chart. If you set limit=0, no series filtering occurs and all values are returned. You can learn more about the limit argument and how it works with the agg argument from the Splunk documentation1. The other options are incorrect because they are not valid values for the limit argument. The limit argument expects an integer value, not a string or a wildcard. You can learn more about the syntax and usage of the timechart command from the Splunk documentation23.

Explanation:
Event types in Splunk are saved searches that categorize data, making it easier to search for specific patterns or criteria within your data. When saving an event type, the search must essentially filter events based on criteria without performing operations that transform or aggregate the data. Here's a breakdown of the options:
A. The searchindex-server_472 sourcetype-BETA_494 code-488 | stats count by code performs an aggregation operation (stats count by code), which makes it unsuitable for saving as an event type. Event types are meant to categorize data without aggregating or transforming it.
B. The searchindex=server_472 sourcetype=BETA_494 code=488 [ | inputlookup append=t servercode.csv]includes a subsearch and input lookup, which is typically used to enrich or filter events based on external data. This complexity goes beyond simple event categorization.
C. The searchindex=server_472 sourcetype=BETA_494 code=488 | stats where code > 200includes a filtering condition within a transforming command (stats), which again, is not suitable for defining an event type due to the transformation of data.
D. The searchindex=server_472 sourcetype=BETA_494 code-488is the correct answer as it purely filters events based on index, sourcetype, and a code field condition without transforming or aggregating the data. This is what makes it suitable for saving as an event type, as it categorizes data based on specific criteria without altering the event structure or content.

Explanation: If a search returns statistics, it can be viewed as a chart2. Statistics are tabular data that show the relationship between two or more fields2. You can create statistics by using commands such as stats, chart or timechart2. You can view statistics as a chart by selecting the Visualization tab in the Search app and choosing a chart type such as column, line or pie2. Therefore, option B is correct, while options A, C and D are incorrect because they are not types of data that can be viewed as a chart.

Explanation:
The correct answer isC. Product, count: addtocart, count: remove, count: purchase, sum: addtocart, sum: remove, sum: purchase1.
In Splunk, thechartcommand is used to create a table or a chart visualization from your data2.Thechartcommand takes at least one function and one field, and optionally another field to group by2.
In the given search, thechartcommand is used with two functions (countandsum), two fields (domainandprice), and two fields to group by (productandaction).Theusenull=fanduseother=foptions are used to exclude null values and other values from the chart2.
Thechartcommand creates a table with headers that match the order of the fields and functions in the command1.The headers for thecountfunction are prefixed withcount:, and the headers for thesumfunction are prefixed withsum:1.The values of theproductandactionfields are used as the suffixes for the headers1.
Therefore, the table headers created by this command areProduct,count: addtocart,count: remove,count: purchase,sum: addtocart,sum: remove, andsum: purchase1.

Explanation:
The correct answer is B. Use the field values in an HTTP error event to create a new ticket in an external system.
A workflow action is a knowledge object that enables a variety of interactions between fields in events and other web resources.Workflow actions can create HTML links, generate HTTP POST requests, or launch secondary searches based on field values1.
There are three types of workflow actions that can be set up using Splunk Web: GET, POST, and Search2.
GET workflow actions create typical HTML links to do things like perform Google searches on specific values or run domain name queries against external WHOIS databases2.
POST workflow actions generate an HTTP POST request to a specified URI.This action type enables you to do things like creating entries in external issue management systems using a set of relevant field values2.
Search workflow actions launch secondary searches that use specific field values from an event, such as a search that looks for the occurrence of specific combinations of ipaddress and http_status field values in your index over a specific time range2.
Therefore, the example that would use a POST workflow action is B. Use the field values in an HTTP error event to create a new ticket in an external system. This example requires sending an HTTP POST request to the URI of the external system with the field values from the event as arguments.
The other examples would use different types of workflow actions. These examples are:
A. Perform an external IP lookup based on a domain value found in events: This example would use a GET workflow action to create a link to an external IP lookup service with the domain value as a parameter.
C. Launch secondary Splunk searches that use one or more field values from selected events: This example would use a Search workflow action to run another Splunk search with the field values from the event as search terms.
D. Open a web browser to look up an HTTP status code: This example would also use a GET workflow action to create a link to a web page that explains the meaning of the HTTP status code.