The transaction command groups events that share a common value in a specified field, such as JSESSIONID, and that occur within a specified time range. The search command filters the results to show only the events that match the given value of JSESSIONID. This search groups the events by JSESSIONID and then shows only the events that have the value SD462K101C2F267 for JSESSIONID2

1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, transaction command.

When creating an event type in Splunk, subsearches are allowed in the search string. Subsearches enable users to perform a secondary search whose results are used as input for the main search. This functionality is useful for more complex event type definitions that require additional filtering or criteria based on another search.

References:

Splunk Docs: About subsearches

Splunk Docs: Event type creation

Splunk Answers: Using subsearches in event types

This search string is not valid: index=web status=50* | chart count over host,status2. This search string uses an invalid syntax for the chart command. The chart command requires one field after the over clause and optionally one field after the by clause. However, this search string has two fields after the over clause separated by a comma. This will cause a syntax error and prevent the search from running. Therefore, option A is correct, while options B and C are incorrect because they are valid search strings that use the chart command correctly.

The transaction command is used when you need to group events and preserve the raw event data. This is essential in situations where context is important and you need to maintain the original details of each event.

References:

Splunk Docs - transaction command

Splunk Answers - When to use transaction vs stats

The correct answer is A. chart, timechart, stats, eventstats.

Transforming commands are commands that change the format of the search results into a table or a chart.They can be used to perform statistical calculations, create visualizations, or manipulate data in various ways1.

Transactions are groups of events that share some common values and are related in some way.Transactions can be defined by using the transaction command or by creating a transaction type in the transactiontypes.conf file2.

Some transforming commands can be used with transactions to create tables or charts based on the transaction fields. These commands include:

chart: This command creates a table or a chart that shows the relationship between two or more fields.It can be used to aggregate values, count occurrences, or calculate statistics3.

timechart: This command creates a table or a chart that shows how a field changes over time.It can be used to plot trends, patterns, or outliers4.

stats: This command calculates 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 fields5.

eventstats: This command calculates summary statistics on the fields in the search results, similar to stats, but it also adds the results to each event as new fields. It can be used to compare events with the overall statistics.

These commands can be applied to transactions by using the transaction fields as arguments. For example, if you have a transaction type named “login” that groups events based on the user field and has fields such as duration and eventcount, you can use the following commands with transactions:

| chart count by user: This command creates a table or a chart that shows how many transactions each user has.

| timechart span=1h avg(duration) by user: This command creates a table or a chart that shows the average duration of transactions for each user per hour.

| stats sum(eventcount) as total_events by user: This command creates a table that shows the total number of events for each user across all transactions.

| eventstats avg(duration) as avg_duration: This command adds a new field named avg_duration to each transaction that shows the average duration of all transactions.

The other options are not valid because they include commands that are not transforming commands or cannot be used with transactions. These commands are:

diff: This command compares two search results and shows the differences between them. It is not a transforming command and it does not work with transactions.

datamodel: This command retrieves data from a data model, which is a way to organize and categorize data in Splunk. It is not a transforming command and it does not work with transactions.

pivot: This command creates a pivot report, which is a way to analyze data from a data model using a graphical interface. It is not a transforming command and it does not work with transactions.

References:

About transforming commands

About transactions

chart command overview

timechart command overview

stats command overview

[eventstats command overview]

[diff command overview]

[datamodel command overview]

[pivot command overview]

In this search, count will appear on the y-axis2. This search uses the chart command to create a chart of the count of events over host for events that have status not equal to 2002. The chart command creates a table with one column for each value of the field after the over clause and one row for each value of the field after the by clause (if any)2. The values in the table are calculated by applying the function before the over clause to the events in each group2. In this case, the chart command creates a table with one column for each host and one row for the count of events for each host. The y-axis of the chart shows the values of the count function applied to each host. Therefore, option C is correct, while options A and B are incorrect because they appear on the x-axis or as labels of the chart.

The search modes determine how Splunk processes your search and displays your results2. There are three search modes: Fast, Smart and Verbose2. The search mode that automatically returns all extracted fields in the fields sidebar is Verbose2. The Verbose mode shows all the fields that are extracted from your events, including default fields, indexed fields and search-time extracted fields2. The fields sidebar is a panel that shows the fields that are present in your search results2. Therefore, option C is correct, while options A and B are incorrect because they are not search modes that automatically return all extracted fields in the fields sidebar.

A delimiter field extraction is a method of extracting fields from data that uses a character or a string to separate fields in each event. A delimiter field extraction can be performed by using the Field Extractor (FX) tool or by editing the props.conf file. A delimiter field extraction can be applied to any file format that uses a delimiter to separate fields, such as CSV, TSV, PSV, etc. A CSV file is a comma-separated values file that uses commas as delimiters. Therefore, a CSV file can be extracted using a delimiter field extraction.

As mentioned before, there are two types of workflow actions: GET and POST1. Both types of workflow actions can be executed from search results by clicking on an event field value that has a workflow action configured for it1. Another type of workflow action is Search, which runs another search based on the field value1. Therefore, options A, B and D are correct, while option C is incorrect because LOOKUP is not a type of workflow action.

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.

Field aliases are alternative names for fields in Splunk. Field aliases can be used to normalize data across different sources and sourcetypes that have different field names for the same concept. For example, you can create a field alias for src_ip that maps to clientip, source_address, or any other field name that represents the source IP address in different sourcetypes. Field aliases can also be used in lookup file definitions to map fields in your data to fields in the lookup file. For example, you can use a field alias for src_ip to map it to ip_address in a lookup file that contains geolocation information for IP addresses. Field alias names do not replace the original field name, but rather create a copy of the field with a different name. Field alias names are case sensitive when used as part of a search, meaning that src_ip and SRC_IP are different fields.

The transaction command is used to group events into transactions based on some common characteristics, such as fields, time, or both. The transaction command can also specify start and end constraints for the transactions, such as a field value that indicates the beginning or the end of a transaction. The stats command is used to calculate summary statistics on the events, such as count, sum, average, etc. The stats command cannot group events based on start and end constraints, but only on fields or time buckets. Therefore, the transaction command should be used instead of the stats command when you need to group events based on start and end constraints.

The eval command is used to create new fields or modify existing fields based on an expression2. The eval command can perform various actions such as calculations, conversions, string manipulations and more2. One of the actions that the eval command can perform is to create or replace an existing field with a new value based on an expression2. For example, | eval status=if(status="200","OK","ERROR") will create or replace the status field with either OK orERROR depending on the original value of status2. Therefore, option B is correct, while options A, C and D are incorrect because they are not actions that the eval command can perform.

The transaction command is a search command that creates a single event from a group of events that share some common characteristics. The transaction command can group events based on fields, time, or both. The transaction command can also create some additional fields for each transaction, such as duration, eventcount, startime, etc. The transaction command does not group a set of transactions based on time, but rather groups a set of events into a transaction based on time. The transaction command does not separate two events based on one or more values, but rather joins multiple events based on one or more values. The transaction command does not return the number of credit card transactions found in the event logs, but rather creates transactions from the events that match the search criteria.

The command sourcetype=access_combined | transaction JSESSIONID does three things:

It filters the events by the sourcetype access_combined, which is a predefined sourcetype for Apache web server logs.

It groups the events by the field JSESSIONID, which is a unique identifier for each user session.

It creates a single event from each group of events that share the same JSESSIONID value. This single event will have some additional fields created by the transaction command, such as duration, eventcount, and startime.

Therefore, the statements B, C, and D are true.

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.

GET workflow actions are custom actions that open a URL link when you click on a field value in your search results. GET workflow actions can be configured with various options, such as label name, base URL, URI parameters, app context, etc. One of the options is to choose whether to open the URL link in the current window or in a new window. GET workflow actions do not have to be configured with POST arguments, as they use GET method to send requests to web servers. Configuration of GET workflow actions does not include choosing a sourcetype, as they do not generate any data in Splunk. Label names for GET workflow actions must include a field name surrounded by dollar signs, as this indicates the field value that will be used to replace the variable in the URL link.

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.

A field alias is a way to assign an alternative name to an existing field without changing the original field name or value2. You can use field aliases to make your field names more consistent or descriptive across different sources or sourcetypes2. When you run a search without any transforming commands in Smart Mode, Splunk automatically identifies and displays interesting fields in your results2. Interesting fields are fields that appear in at least 20 percent of events or have high variability among values2. If you have created a field alias based on an original field, both the original field name and the alias name will appear in the Interesting Fields list if they meet these criteria2. However, only one of them will appear in each event depending on which one you have specified in your search string2. Therefore, option B is correct, while options A, C and D are incorrect.

 The data model command allows you to run searches on data models that have been accelerated1. The syntax for using the data model command is | datamodel [search ]1. Therefore, option A is the correct way to use the data model command to search fields in the data model within the web dataset. Options B and C are incorrect because they do not follow the syntax for the data model command. Option D is incorrect because it does not use the data model command at all.

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 Splunk Common Information Model (CIM) add-on is a collection of pre-built data models and knowledge objects that help you normalize your data from different sources and make it easier to analyze and report on it3. The CIM add-on includes pre-configured data models that cover various domains such as Alerts, Email, Database, Network Traffic, Web and more3. Therefore, option B is correct. The CIM add-on also includes fields and event category tags that define the common attributes and labels for the data models3. Therefore, option C is correct. The CIM add-on does not include custom visualizations or automatic data model acceleration. Therefore, options A and D are incorrect.

Tags are aliases or alternative names for field values in Splunk. They can make your data more understandable by using common or descriptive terms instead of cryptic or technical terms. For example, you can tag a field value such as “200” with “OK” or “success” to indicate that it is a HTTP status code for a successful request. Tags are case sensitive, meaning that “OK” and “ok” are different tags. Tags are created at search time, meaning that they are applied when you run a search on your data. Tags are searched by using the syntax tag::, where  is the name of the tag you want to search for.

Data models and pivot are both knowledge objects in Splunk that allow you to analyze and visualize your data in different ways. 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. Pivot is a user interface that allows you to create data visualizations that present different aspects of a data model. Pivot does not require users to input SPL searches on data models, but rather lets them select options from menus and forms. Data models are not created out of datasets called pivots, but rather pivots are created from datasets in data models.

Search workflow actions are custom actions that run a search when you click on a field value in your search results. Search workflow actions can be configured with various options, such as label name, search string, time range, app context, etc. One of the options is to define the time range of the search when creating the workflow action. You can choose from predefined time ranges, such as Last 24 hours, Last 7 days, etc., or specify a custom time range using relative or absolute time modifiers. Search workflow actions do not run as real-time searches by default, but rather use the same time range as the original search unless specified otherwise. Search workflow actions cannot be configured as scheduled searches, as they are only triggered by user interaction. Search workflow actions can be configured with any valid search string that includes any search command, such as transaction.

The eval command supports a number of functions that you can use in your expressions to performcalculations, conversions, string manipulations and more2. One of the eval command functions is tostring(), which converts a numeric value to a string value2. Therefore, option D is correct, while options A, B and C are incorrect because they are not valid eval command functions.

https://docs.splunk.com/Documentation/Splunk/8.1.0/SearchReference/ConversionFunctions#tostring.28X.2CY.29

The tostring function in the eval command converts a numeric value to a string value. It can take an optional second argument that specifies the format of the string value. Some of the possible formats are:

hex: converts the numeric value to a hexadecimal string.

commas: adds commas to separate thousands in the numeric value.

duration: converts the numeric value to a human-readable duration string, such as “2h 3m 4s”.

Therefore, the formats A, B, and D can be used with the tostring function.

POST workflow actions are custom actions that send a POST request to a web server when you click on a field value in your search results. POST workflow actions can be configured with various options, such as label name, base URL, URI parameters, post arguments, app context, etc. One of the options that are required to create a POST workflow action is post arguments. Post arguments are key-value pairs that are sent in the body of the POST request to provide additional information to the web server. Post arguments can include field values from your data by using dollar signs around the field names.

This search uses the transaction command to group events that share a common value for JSESSIONID into transactions1. The transaction command assigns a duration field to each transaction, which is the difference between the latest and earliest timestamps of the events in the transaction1. The search then uses the timechart command to create a time-series chart of the average duration of each transaction1. Therefore, option A is correct because it describes the search accurately. Option B is incorrect because the search does not use the stats command or the pause field. Option C is incorrect because the transaction command does not require the startswith and endswith options, although they can be used to specify how to identify the beginning and end of a transaction1. Option D is incorrect because the transaction command does not have to be the last command in the search pipeline, although it is often used near the end of a search1.

The Field Extractor (FX) is a tool that helps you extract fields from your events using a graphical interface2. You can create a report using a custom field extracted by the FX and share it with other users in your organization2. However, if another user runs the shared report and no results are returned, there could be two possible reasons. One reason is that the extraction is private, which means that only you can see and use the extracted field2. To make the extraction available to other users, you need to make it global or app-level2. Therefore, option C is correct. Another reason is that the other user does not have access to the index where the events are stored2. To fix this issue, you need to grant the appropriate permissions to the other user for the index2. Therefore, option D is correct. Options A and B are incorrect because they are not related to the field extraction or the report.

After manually editing a regular expression (regex) that was created using the Field Extractor (FX) UI, it is no longer possible to edit the field extraction in the FX UI. The FX UI is a tool that helps you extract fields from your data using delimiters or regular expressions. The FX UI can generate a regex for you based on your selection of sample values or you can enter your own regex in the FX UI. However, if you edit the regex manually in the props.conf file, the FX UI will not be able to recognize the changes and will not let you edit the field extraction in the FX UI anymore. You will have to use the props.conf file to make any further changes to the field extraction. Changes made manually cannot be reverted in the FX UI, as the FX UI does not keep track of the changes made in the props.conf file. It is possible to manually edit a regex that was created using the FX UI, as long as you do it in the props.conf file.

Therefore, only statement B is true about manually editing a regex.

The search below groups events by two or more fields (clientip and host), creates transactions with start and end constraints (maxspan=30s and maxpause=5s), and calculates the duration of each transaction.

index=main | transaction clientip host maxspan=30s maxpause=5s

The search does the following:

It filters the events by the index main, which is a default index in Splunk that contains all data that is not sent to other indexes.

It uses the transaction command to group events into transactions based on two fields: clientip and host. The transaction command creates new events from groups of events that share the same clientip and host values.

It specifies the start and end constraints for the transactions using the maxspan and maxpause arguments. The maxspan argument sets the maximum time span between the first and last events in a transaction. The maxpause argument sets the maximum time span between any two consecutive events in a transaction. In this case, the maxspan is 30 seconds and the maxpause is 5 seconds, meaning that any transaction that has a longer time span or pause will be split into multiple transactions.

It creates some additional fields for each transaction, such as duration, eventcount, startime, etc. The duration field shows the time span between the first and last events in a transaction.

A space is an implied AND in a search string, which means that it acts as a logical operator that returns events that match both terms on either side of the space2. For example, status=200 method=GET will return events that have both status=200 and method=GET2. Therefore, option B is correct, while options A, C and D are incorrect because they are not implied by a space in a search string.

A workflow action is a link that appears when you click an event field value in your search results1. A workflow action can open a web page or run another search based on the field value1. There are two types of workflow actions: GET and POST1. A GET workflow action appends the field value to the end of a URI and opens it in a web browser1. A POST workflow action sends the field value as part of an HTTP request to a web server1. You can configure a workflow action to open a web page in either the same window or a new window1. Therefore, option D is correct, while options A, B and C are incorrect.

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 thecumulative 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.

A calculated field in Splunk is designed to automatically add fields at search time using an eval expression. This feature allows users to define new fields based on existing data without needing to manually include an evalcommand in every search. Calculated fields simplify repeated search tasks by embedding the eval logic directly into the field configuration.

References:

Splunk Docs: Calculated fields

Splunk Answers: Purpose of calculated fields

One of the valid options to speed up reports is to edit acceleration, which means that you can enable summary indexing or data model acceleration for your reports to improve their performance2. Summary indexing allows you to create reports that run over large amounts of data by storing the results of scheduled searches in a summary index and using that index for faster reporting2. Data model acceleration allows you to create reports that use data models by creating and storing summaries of the data model datasets and using them for faster reporting2. Therefore, option C is correct, while options A, B and D are incorrect because they are not options to speed up reports.

To use a search macro in a search string, you need to place a back tick character (`) before and after the macro name1. You also need to use the same number of arguments as defined in the macro2. The macro weekly sales (2) has two arguments:PriceandAmountSold. Therefore, you need to provide two values for these arguments when you call the macro.

The option A is incorrect because it uses parentheses instead of back ticks around the macro name. The option B is incorrect because it uses underscores instead of spaces in the macro name. The option D is incorrect because it uses spaces instead of commas to separate the argument values.

 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.

In Splunk, the NOT operator is used to exclude events from your search results. The search index=network NOT StatusCode=200 will return all events in the ‘network’ index where the StatusCode is not 200. This includes events where the StatusCode field is present and has a value other than 200, as well as events where the StatusCode field is not present at all.

References:The use of the NOT operator in SPL (Search Processing Language) is consistent with the information provided in the Splunk documentation and resources, which describe how to generate efficient searches and make the most of Splunk’s capabilities

The search below would limit an “alert” tag to the “host” field.

tag::host=alert

The search does the following:

It uses tag syntax to filter events by tags. Tags are custom labels that can be applied to fields or field values to provide additional context or meaning for your data.

It specifies tag::host=alert as the tag filter. This means that it will only return events that have an “alert” tag applied to their host field or host field value.

It uses an equal sign (=) to indicate an exact match between the tag and the field or field value.

The correct answer is C. A macro is a portion of a search that can be reused in multiple places.

A macro is a way to reuse a piece of SPL code in different searches. A macro can be any part of a search, such as an eval statement or a search term, and does not need to be a complete command. A macro can also take arguments, which are variables that can be replaced by different values when the macro is called.A macro can also contain another macro within it, which is called a nested macro1.

To create a macro, you need to define its name, definition, arguments, and description in the Settings > Advanced Search > Search Macros page in Splunk Web or in the macros.conf file.To use a macro in a search, you need to enclose the macro name in backtick characters (`) and provide values for the arguments if any1.

For example, if you have a macro named my_macro that takes one argument named object and has the following definition:

search sourcetype=object

You can use it in a search by writing:

my_macro(web)

This will expand the macro and run the following SPL code:

search sourcetype=web

The benefits of using macros are that they can simplify complex searches, reduce errors, improve readability, and promote consistency1.

The other options are not correct because they describe other types of knowledge objects in Splunk, not macros. These objects are:

A. An event type is a method of categorizing events based on a search. An event type assigns a label to events that match a specific search criteria.Event types can be used to filter and group events, create alerts, or generate reports2.

B. A field alias is a way to associate an additional (new) name with an existing field name. A field alias can be used to normalize fields from different sources that have different names but represent the same data.Field aliases can also be used to rename fields for clarity or convenience3.

D. An alert is a knowledge object that enables you to schedule searches for specific events and trigger actions when certain conditions are met.An alert can be used tomonitor your data for anomalies, errors, or other patterns of interest and notify you or others when they occur4.

References:

About event types

About field aliases

About alerts

Define search macros in Settings

Use search macros in searches

The correct answer is B. A field added by an automatic lookup.

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 use any field as a source, as long as the field is extracted before the calculated field is defined1.

An automatic lookup is a way to enrich events with additional fields from an external source, such as a CSV file or a database.An automatic lookup can add fields to eventsbased on the values ofexisting fields, such as host, source, sourcetype, or any other extracted field2.An automatic lookup is performed before the calculated fields are defined, so the fields added by the lookup can be used as sources for the calculated fields3.

Therefore, a calculated field can use a field added by an automatic lookup as a source.

References:

About calculated fields

About lookups

Search time processing

The CIM Add-on manual contains the descriptions of the data models that come with the Splunk Common Information Model (CIM) Add-on, as well as how to set up, use, and customize the add-on.

References

CIM Add-on manual

Splunk Common Information Model (CIM) | Splunkbase

Understand and use the Common Information Model Add-on - Splunk

When using the transaction command in Splunk, the default maximum span between events is set to unlimited. This is indicated by the default value of maxspan=-1, which corresponds to an “all time” time range.

Event types allow users to assign labels to events based on predefined search strings. This helps categorize data and makes it easier to reference specific sets of events in future searches.

References:

Splunk Docs - Event types

The correct answer is B. Delimiters. This is because the delimiters method is designed for structured event data, such as data from files with headers, where all of the fields in the events are separated by a common delimiter, such as a comma or space. You can select a sample event, identify the delimiter, and then rename the fields that the field extractor finds.You can learn more about the delimiters method from the Splunk documentation1. The other options are incorrect because they are not suitable for comma-separated data. The regular expression method works best with unstructured event data, where you select and highlight one or more fields to extract from a sample event, and the field extractor generates a regular expression that matches similar events and extracts the fields from them. The eval expression is a command that lets you calculate new fields or modify existing fields using arithmetic, string, and logical operations. The table extraction is a feature that lets you extract tabular data from PDF files or web pages.You can learn more about these methods from the Splunk documentation23.

Thefillnullcommand in Splunk is used to handle missing data within search results. It plays a crucial role in data normalization and preparation, especially before performing statistical analyses or visualizations.

A.Replace empty values with a specified value:This is the correct answer. Thefillnullcommand is specifically designed to replace null values (empty values) with a specified default value. This is particularly useful in ensuring consistency within your data, especially when performing operations that require numerical values or when you want to distinguish between genuinely missing data and zeroes, for instance.

Example Usage:... | fillnull value=0This command would replace all null values in the search results with 0.

The fillnull command replaces null values in one or more fields with a specified value. The values option allows you to specify a comma-separated list of values to fill the null values in the corresponding fields. The fields option allows you to specify a comma-separated list of fields to apply the fillnull command to. The eval statement in the question uses the if and isnull functions to check if field1 and field2 have null values and replace them with 0 and “NO-VALUE” respectively. The equivalent expression using fillnull is to use the values option to specify 0 and “NO-VALUE” and the fields option to specify field1 and field22

1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, fillnull command.

 The following commands are used when creating visualizations: geom, geostats, and iplocation. Visualizations are graphical representations of data that show trends, patterns, or comparisons. Visualizations can have different types, such as charts, tables, maps, etc. Visualizations can be created by using various commands that transform the data into a suitable format for the visualization type. Some of the commands that are used when creating visualizations are:

geom: This command is used to create choropleth maps that show geographic regions with different colors based on some metric. The geom command takes a KMZ file as an argument that defines the geographic regions and their boundaries. The geom command also takes a field name as an argument that specifies the metric to use for coloring the regions.

geostats: This command is used to create cluster maps that show groups of events with different sizes and colors based on some metric. The geostats command takes a latitude and longitude field as arguments that specify the location of the events. The geostats command also takes a statistical function as an argument that specifies the metric to use for sizing and coloring the clusters.

iplocation: This command is used to create location-based visualizations that show events with different attributes based on their IP addresses. The iplocation command takes an IP address field as an argument and adds some additional fields to the events, such as Country, City, Latitude, Longitude, etc. The iplocation command can be used with other commands such as geom or geostats to create maps based on IP addresses.

The chart command with sum(price) by product, region will return a table where the total revenue (price) is aggregated (sum) for each product and sales region. This is the correct way to aggregate data in Splunk.

References:

Splunk Docs - chart command

https://docs.splunk.com/Documentation/Splunk/latest/SearchReference/CommonEvalFunctions

The eval command function tostring() is valid. The tostring() function converts a numeric value to a string value. For example, tostring(3.14) returns “3.14”. The other functions are not valid eval command functions.

In Splunk, you can create an event type from the search window by running a search that would make a good event type, then clicking Save As and selecting Event Type1. This opens the Save as Event Type dialog, where you can provide the event type name and optionally apply tags to it1.

You can also create an event type by editing the eventtypes.conf file and adding a new stanza1. Each stanza in the eventtypes.conf file represents an event type1. The stanza name isthe name of the event type, and the search attribute specifies the search string that defines the event type1.

It’s important to note that while you can use the eventtype command in a search to find events associated with a specific event type, adding | eventtype to the SPL and executing the search does not create a new event type1. Similarly, clicking Event Actions > Build Event Type in an event’s detail dropdown does not create a new event type1.

The correct answer is D.

A search macro is a way to reuse a piece of SPL code in different searches. A search macro can take arguments, which are variables that can be replaced by different values when the macro is called.A search macro can also contain another search macro within it, which is called a nested macro1.

To represent an argument in a macro definition, you need to use the dollar sign ($) character to enclose the argument name. For example, if you want to create a search macro that takes one argument named “object”, you can use the following syntax:

[my_macro(object)] search sourcetype=object

This will create a search macro named my_macro that takes one argument named object. When you call the macro in a search, you need to provide a value for the object argument, such as:

my_macro(web)

This will replace the object argument with the value web and run the following SPL code:

search sourcetype=web

The other options are not correct because they use quotation marks (’ or ") or percentage signs (%) to represent arguments, which are not valid syntax for macro arguments. These characters will be interpreted as literal values instead of variables.

References:

Use search macros in searches

https://docs.splunk.com/Documentation/Splunk/8.0.2/Search/Abouttransactions

In other cases, it's usually better to use the stats command, which performs more efficiently, especially in a distributed environment. Often there is a unique ID in the events and stats can be used.

When using the Field Extractor (FX) in Splunk to perform field extraction, any consistent character can be used as a delimiter. The Field Extractor allows users to define how fields are separated in the raw event data, and as long as the delimiter is consistent, the FX tool can parse and extract the fields correctly.

References:

Splunk Docs: Field Extractor

Splunk Answers: Field extraction delimiters

In Splunk, a data model's root dataset is the foundational element upon which the rest of the data model is built. The root dataset can be of various types, including search, transaction, or event-based datasets. One of the key features of the root dataset is that it automatically inherits the knowledge objects associated with its base search. These knowledge objects include field extractions, lookups, aliases, and calculated fields that are defined for the base search, ensuring that the root dataset has all necessary contextual information from the outset. This allows users to build upon this dataset with additional child datasets and objects without having to redefine the base search’s knowledge objects.

When you mouse over and click to add a search term from the Fields sidebar or from an event in your search results, Splunk automatically adds the term to your search string with an implied ANDoperator2. However, this does not apply to some Boolean operators such as OR, NOT and parentheses (). These operators are not implied when you add a search term and you have to type them manually ifyou want to use them in your search string2. Therefore, options A, B and D are correct, while option C is incorrect because AND is implied when you add a search term.

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.

References:

Splexicon:Workflowaction

About workflow actions in Splunk Web

A calculated field in Splunk is created using an eval expression, which allows users to perform calculations or transformations on field values during search time.

References:

Splunk Docs - Calculated fields

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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

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”.

References:

where command usage

Search command cheatsheet