Splunk SPLK-1002 Practice Test - Questions Answers, Page 22

The following search would create a graph similar to the one below:

index_internal sourcetype=Savesplunker | fields sourcetype, status | transaction status maxspan=1d | timechart count by status

The search does the following:

It uses index_internal to specify the internal index that contains Splunk logs and metrics.

It uses sourcetype=Savesplunker to filter events by the sourcetype that indicates the Splunk Enterprise Security app.

It uses fields sourcetype, status to keep only the sourcetype and status fields in the events.

It uses transaction status maxspan=1d to group events into transactions based on the status field with a maximum time span of one day between the first and last events in a transaction.

It uses timechart count by status to create a time-based chart that shows the count of transactions for each status value over time.

The graph shows the following:

It is a line graph with two lines, one yellow and one blue.

The x-axis is labeled with dates from Wed, Apr 4, 2018 to Tue, Apr 10, 2018.

The y-axis is labeled with numbers from 0 to 15.

The yellow line represents ''shipped'' and the blue line represents ''success''.

The yellow line has a steady increase from 0 to 15, while the blue line has a sharp increase from 0 to 5, then a decrease to 0, and then a sharp increase to 10.

The graph is titled ''Type''.

Therefore, option C is the correct answer.

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.

By default, acceleration is determined automatically based on the data source in the Splunk Common Information Model (CIM) add-on. The Splunk CIM Add-on is an app that provides common data models for various domains, such as network traffic, web activity, authentication, etc. The CIM Add-on allows you to normalize and enrich your data using predefined fields and tags. The CIM Add-on also allows you to accelerate your data models for faster searches and reports. Acceleration is a feature that pre-computes summary data for your data models and stores them in tsidx files. Acceleration can improve the performance and efficiency of your searches and reports that use data models.

By default, acceleration is determined automatically based on the data source in the CIM Add-on. This means that Splunk will decide whether to enable or disable acceleration for each data model based on some factors, such as data volume, data type, data model complexity, etc. However, you can also manually enable or disable acceleration for each data model by using the Settings menu or by editing the datamodels.conf file.

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.

The correct answer is C. Events where values of field A are equal to values of field B.

The where command is used to filter the search results based on an expression that evaluates to true or false. The where command can compare two fields, two values, or a field and a value. The where command can also use functions, operators, and wildcards to create complex expressions1.

The syntax for the where command is:

| where <expression>

The expression can be a comparison, a calculation, a logical operation, or a combination of these. The expression must evaluate to true or false for each event.

To compare two fields with the where command, you need to use the field names without any quotation marks. For example, if you want to find events where the values for the field A match the values for the field B, you can use the following syntax:

| where A=B

This will return only the events where the two fields have the same value.

The other options are not correct because they use different syntax or fields that are not related to the where command. These options are:

A) Events that contain the string value where A=B: This option uses the string value where A=B as a search term, which is not valid syntax for the where command. This option will return events that have the literal text ''where A=B'' in them.

B) Events that contain the string value A=B: This option uses the string value A=B as a search term, which is not valid syntax for the where command. This option will return events that have the literal text ''A=B'' in them.

D) Events where field A contains the string value B: This option uses quotation marks around the value B, which is not valid syntax for comparing fields with the where command. Quotation marks are used to enclose phrases or exact matches in a search2. This option will return events where the field A contains the string value ''B''.

where command usage

Search command cheatsheet

The correct answer is A. When a log file has values that are separated by the same character, for example, commas.

The Field Extractor (FX) is a utility in Splunk Web that allows you to create new fields from your events by using either regular expressions or delimiters. The FX provides a graphical interface that guides you through the steps of defining and testing your field extractions1.

The FX supports two field extraction methods: regular expression and delimited. The regular expression method works best with unstructured event data, such as logs or messages, that do not have a consistent format or structure. You select a sample event and highlight one or more fields to extract from that event, and the FX generates a regular expression that matches similar events in your data set and extracts the fields from them1.

The delimited method is designed for structured event data: data from files with headers, where all of the fields in the events are separated by a common delimiter, such as a comma, a tab, or a space. You select a sample event, identify the delimiter, and then rename the fields that the FX finds1.

Therefore, you would select the delimited field extraction method when you have a log file that has values that are separated by the same character, for example, commas. This method will allow you to easily extract the fields based on the delimiter without writing complex regular expressions.

The other options are not correct because they are not suitable for the delimited field extraction method. These options are:

B) When a log file contains empty lines or comments: This option does not indicate that the log file has a structured format or a common delimiter. The delimited method might not work well with this type of data, as it might miss some fields or include some unwanted values.

C) With structured files such as JSON or XML: This option does not require the delimited method, as Splunk can automatically extract fields from JSON or XML files by using indexed extractions or search-time extractions2. The delimited method might not work well with this type of data, as it might not recognize the nested structure or the special characters.

D) When the file has a header that might provide information about its structure or format: This option does not indicate that the file has a common delimiter between the fields. The delimited method might not work well with this type of data, as it might not be able to identify the fields based on the header information.

Build field extractions with the field extractor

Configure indexed field extraction

The correct answer is D. eval.

A calculated field is a field that is added to events at search time by using an eval expression. A calculated field can use the values of two or more fields that are already present in the events to perform calculations. A calculated field can be defined with Splunk Web or in the props.conf file. They can be used in searches, reports, dashboards, and data models like any other extracted field1.

A calculated field is a shortcut for performing repetitive, long, or complex transformations using the eval command. The eval command is used to create or modify fields by using expressions. The eval command can perform mathematical, string, date and time, comparison, logical, and other operations on fields or values2.

For example, if you want to create a new field named total that is the sum of two fields named price and tax, you can use the eval command as follows:

| eval total=price+tax

However, if you want to use this new field in multiple searches, reports, or dashboards, you can create a calculated field instead of writing the eval command every time. To create a calculated field with Splunk Web, you need to go to Settings > Fields > Calculated Fields and enter the name of the new field (total), the name of the sourcetype (sales), and the eval expression (price+tax). This will create a calculated field named total that will be added to all events with the sourcetype sales at search time. You can then use the total field like any other extracted field without writing the eval expression1.

The other options are not correct because they are not related to calculated fields. These options are:

A) transaction: This command is used to group events that share some common values into a single record, called a transaction. A transaction can span multiple events and multiple sources, and can be useful for correlating events that are related but not contiguous3.

B) lookup: This command is used to enrich events with additional fields from an external source, such as a CSV file or a database. A lookup can add fields to events based on the values of existing fields, such as host, source, sourcetype, or any other extracted field.

C) stats: This command is used to calculate summary statistics on the fields in the search results, such as count, sum, average, etc. It can be used to group and aggregate data by one or more fields.

About calculated fields

eval command overview

transaction command overview

[lookup command overview]

[stats command overview]

The correct answer is C. Product, count: addtocart, count: remove, count: purchase, sum: addtocart, sum: remove, sum: purchase1.

In Splunk, the chart command is used to create a table or a chart visualization from your data2. The chart command takes at least one function and one field, and optionally another field to group by2.

In the given search, the chart command is used with two functions (count and sum), two fields (domain and price), and two fields to group by (product and action). The usenull=f and useother=f options are used to exclude null values and other values from the chart2.

The chart command creates a table with headers that match the order of the fields and functions in the command1. The headers for the count function are prefixed with count:, and the headers for the sum function are prefixed with sum:1. The values of the product and action fields are used as the suffixes for the headers1.

Therefore, the table headers created by this command are Product, count: addtocart, count: remove, count: purchase, sum: addtocart, sum: remove, and sum: purchase1.

In Splunk, Pivot is a tool that allows you to report on a specific data set without using the Splunk Search Processing Language (SPL)1.You can use a drag-and-drop interface to design and generate pivots that present different aspects of your data in the form of tables, charts, and other visualizations12.

One of the features of Pivot is that it allows you to save your reports1.This can be useful when you want to reuse a report or share it with others1.Therefore, it's not true that users cannot share visualizations created with Pivot or that they must use SPL to find events in a Pivot12.It's also not true that users cannot create visualizations with Pivot, as creating visualizations is one of the main functions of Pivot12.