Splunk SPLK-1002 Splunk Core Certified Power User Exam Exam Practice Test

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 eval command supports a number of functions that you can use in your expressions to perform calculations, 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.

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.

As mentioned before, a calculated field is a field that you create based on the value of another field or fields2. A calculated field can be based on extracted fields, which are fields that are extracted from your raw data using various methods such as regular expressions, delimiters or key-value pairs2. Therefore, option B is correct, while options A, C and D are incorrect because they are not types of fields that a calculated field can be based on.

A macro is a way to save a commonly used search string as a variable that you can reuse in other searches1. When you create a macro, you can define arguments that are placeholders for values that you specify at execution time1. The argument values are used to resolve the search string when the macro is invoked, not when it is created1. Therefore, statements B and C are true, while statements A and D are false.

The eval command is used to create new fields or modify existing fields based on an expression2. The sort command is used to sort the results by one or more fields in ascending or descending order2. If you want to convert numeric field values to strings and also sort on those values, you should use the sort command first, then use the eval command to convert the values to strings2. This way, the sort command will use the original numeric values for sorting, rather than the converted string values which may not sort correctly. Therefore, option C is correct, while options A, B and D are incorrect.

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.

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.

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.

Selected fields are fields that you choose to display in your search results by clicking on them in the Fields sidebar or by using the fields command2. Selected fields are displayed below each event in the search results, along with their values2. Therefore, option A is correct, while options B, C and D are incorrect because they are not places where selected fields are displayed.

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.

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 search string below creates a table of the total count of mysterymeat corndogs split by user.

| stats count by user | where corndog=mysterymeat

The search string does the following:

• It uses the stats command to calculate the count of events for each value of the user field. The stats command creates a table with two columns: user and count.
• It uses the where command to filter the results by the value of the corndog field. The where command only keeps the rows where corndog equals mysterymeat.

Therefore, the search string creates a table of the total count of mysterymeat corndogs split by user.

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.

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 or ERROR 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 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.

Auto-Extracted fields in Splunk Data Models are derived directly from the indexed data based on the existing fields within the events. These fields are identified and extracted by Splunk automatically, without the need for explicit field extractions configured by the user. Understanding the characteristics of Auto-Extracted fields is crucial for effectively managing Data Models and utilizing them in Pivot tables for analysis.

A. Auto-Extracted fields can be hidden in Pivot. This is true. When building a Data Model, you have the option to hide certain fields from appearing in Pivot, making the Pivot table cleaner and more focused on the fields that are most relevant for analysis. This helps in reducing clutter and focusing on the data that matters most to the users.

B. Auto-Extracted fields can have their data type changed. This statement is not typically accurate for Auto-Extracted fields. The data type of an Auto-Extracted field is determined by Splunk based on the field's content in the indexed data. While you can assign a type to a field when you manually create a field in a data model, the inherent data type of Auto-Extracted fields is not something that is changed within the Data Model itself.

C. Auto-Extracted fields can be given a friendly name for use in Pivot. This is correct. Within Data Models, you can assign a more user-friendly, descriptive name to an Auto-Extracted field. This feature is particularly useful in making Data Models more intuitive and easier to use for those who may not be familiar with the original field names or when the original field names are not descriptive or user-friendly.

D. Auto-Extracted fields can be added if they already exist in the dataset with constraints. This is true. Auto-Extracted fields are based on fields that already exist in the data. When you define a dataset within a Data Model, you can apply constraints to narrow down the events that the dataset includes. The Auto-Extracted fields are then identified from this constrained dataset. This means that the fields must already be present in the data that meets the dataset's constraints to be available for auto-extraction.

In summary, Auto-Extracted fields in Splunk Data Models offer a flexible and efficient way to utilize existing data fields within Pivot tables, with options to rename them for clarity and hide unnecessary fields to streamline data analysis.

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.

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 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.

The fields sidebar is a panel that shows the fields that are present in your search results2. The fields sidebar has two sections: selected fields and interesting fields2. Selected fields are fields that you choose to display in your search results by clicking on them in the fields sidebar or by using the fields command2. Interesting fields are fields that appear in at least 20 percent of events or have high variability among values2. For each field in the fields sidebar, you can select one of the following options: events with this field, rare values, top values by time or top values2. If you select rare values, Splunk will automatically pipe your search results to the rare command, which shows the least common values of a field2. Therefore, option B is correct, while options A, C and D are incorrect because they do not pipe your search results to the rare command.

Stacked charts represent a series in a single bar with multiple sections. A chart is a graphical representation of data that shows trends, patterns, or comparisons. A chart can have different types, such as column, bar, line, area, pie, etc. A chart can also have different modes, such as split-series, multi-series, stacked, etc. A stacked chart is a type of chart that shows multiple series in a single bar or area with different sections for each series

The statement that fields extracted using the Field Extractor persist as knowledge objects is true. The Field Extractor (FX) is a graphical tool that allows you to extract fields from raw events using regular expressions or delimiters. The fields extracted by the FX are saved as knowledge objects that can be used in future searches or shared with other users.

The building block of a data model. Each data model is composed of one or more data model datasets. Each dataset within a data model defines a subset of the dataset represented by the data model as a whole.

Data model datasets have a hierarchical relationship with each other, meaning they have parent-child relationships. Data models can contain multiple dataset hierarchies. There are three types of dataset hierarchies: event, search, and transaction.

https://docs.splunk.com/Splexicon:Datamodeldataset

The regular expression mode of Field Extractor (FX) should be used for data with multiple, different characters separating fields or for unstructured data. 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.ReferencesSee Build field extractions with the field extractor - Splunk Documentation and Field Extractor: Select Method step - Splunk Documentation.

The correct answer is B. The value for the productName field because it appears first.

The coalesce function is an eval function that takes an arbitrary number of arguments and returns the first value that is not null. A null value means that the field has no value at all, while an empty value means that the field has a value, but it is “” or zero-length1.

The coalesce function can be used to combine fields that have different names but represent the same data, such as IP address or user name. The coalesce function can also be used to rename fields for clarity or convenience2.

The syntax for the coalesce function is:

coalesce(,,…)

The coalesce function will return the value of the first field that is not null in the argument list. If all fields are null, the coalesce function will return null.

For example, if you have a set of events where the IP address is extracted to either clientip or ipaddress, you can use the coalesce function to define a new field called ip, that takes the value of either clientip or ipaddress, depending on which is not null:

| eval ip=coalesce(clientip,ipaddress)

In your example, you have a set of events where the product name is extracted to either productName or productid, and you use the coalesce function to define a new field called productINFO, that takes the value of either productName or productid, depending on which is not null:

| eval productINFO=coalesce(productName,productid)

If both productName and productid fields have values for a given event, the coalesce function will return the value of the productName field because it appears first in the argument list. The productid field will be ignored by the coalesce function.

Therefore, the value for the productName field will be used to populate the productINFO field if both fields have values for a given event.

References:

• Search Command> Coalesce
• USAGE OF SPLUNK EVAL FUNCTION : COALESCE

https://docs.splunk.com/Documentation/Splunk/8.1.0/SearchReference/Chart

https://docs.splunk.com/Documentation/Splunk/8.1.0/SearchReference/Stats

https://docs.splunk.com/Documentation/Splunk/8.0.2/SearchReference/Eval

The eval command calculates an expression and puts the resulting value into a search results field.

• If the field name that you specify does not match a field in the output, a new field is added to the search results.
• If the field name that you specify matches a field name that already exists in the search results, the results of the eval expression overwrite the values in that field.

A report is a way to save a search and its results in a format that you can reuse and share with others2. A report is also a type of knowledge object, which is an entity that you create to add knowledge to your data and make it easier to search and analyze2. Therefore, option A is correct. A report can be scheduled, which means that you can configure it to run at regular intervals and send the results to yourself or others via email or other methods2. Therefore, option B is correct. A report can run a script, which means that you can specify a script file to execute when the report runs and use it to perform custom actions or integrations2. Therefore, option C is correct. Therefore, option D is correct because all of the above statements are true for reports.

The statement that pivot is used for creating reports and dashboards is true. Pivot is a graphical interface that allows you to create tables, charts, and visualizations from data models. Data models are structured datasets that define how data is organized and categorized. Pivot does not create datasets, but uses existing ones.

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 events based on the values of existing 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 correct answer is D. Search.

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 workflow action type that performs a secondary search is Search.

References:

• Splexicon:Workflowaction
• About workflow actions in Splunk Web

The correct answer is B. Pivot1.

In Splunk, Pivot is a tool that uses data models to generate reports and dashboard panels without the need for users to write or understand Splunk’s Search Processing Language (SPL)1. Data models enable users of Pivot to create compelling reports and dashboards1. When a Pivot user designs a pivot report, they select the data model that represents the category of event data that they want to work with1. Then they select a dataset within that data model that represents the specific dataset on which they want to report1. This makes Pivot a powerful tool for users who need to create visualizations but do not have a deep understanding of SPL1.

The timeline is a graphical representation of your search results that shows the distribution of events over time2. You can use the timeline to zoom in or out of a specific time range or to select one or more bars on the timeline to filter your results by that time range2. However, these actions will not re-run the search, but rather refine the existing results based on the selected time range2. Therefore, options B, C and D are correct, while option A is incorrect because zooming out will re-run the search with a broader time range.

The correct answer is B. Reusable pieces of search processing language.

The explanation is as follows:

• Search macros are knowledge objects that allow you to insert chunks of SPL into other searches12.
• Search macros can be any part of a search, such as an eval statement or a search term, and do not need to be a complete command12.
• You can also specify whether the macro field takes any arguments and define validation expressions for them12.
• Search macros can help you make your SPL searches shorter and easier to understand3.
• To use a search macro in a search string, you need to put a backtick character () before and after the macro name[^1^][1]. For example, mymacro`.

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 .

Data model attributes are fields that are added to a dataset from search time field extractions, calculated fields, lookups, or aliases. They can be created within the data model editor or inherited from a parent dataset. They can be edited or removed unless they are required by the data model. They can be added to any type of dataset, not just root search datasets.ReferencesSee About data models, [Define data model attributes], and [Edit data model datasets] in the Splunk Documentation.

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 to monitor 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 transaction and stats commands are two ways to group events from one or more data sources based on common fields or time ranges. The transaction command creates a single event out of a group of related events, while the stats command calculates summary statistics over a group of events. The eval and coalesce commands are used to create or combine fields, not to group events. The format command is used to format the results of a subsearch, not to group events. The top and rare commands are used to rank the most or least common values of a field, not to group events23

1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, transaction command. 3: Splunk Documentation, stats command.

https://docs.splunk.com/Documentation/Splunk/8.1.2/SearchReference/Chart

The Field Extractor (FX) is a tool that helps you extract fields from your events using a graphical interface or by manually editing the regular expression2. The FX allows you to create field extractions that persist as knowledge objects, which are entities that you create to add knowledge to your data and make it easier to search and analyze2. Field extractions are methods that extract fields from your raw data using various techniques such as regular expressions, delimiters or key-value pairs2. When you create a field extraction using the FX, you can save it as a knowledge object that applies to your data at search time2. You can also manage and share your field extractions with other users in your organization2. Therefore, option C is correct, while options A, B and D are incorrect because they do not describe the use of the FX.

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

The correct answer is B. Event category tags. This is because the CIM add-on contains a collection of preconfigured data models that you can apply to your data at search time. Each data model in the CIM consists of a set of field names and tags that define the least common denominator of a domain of interest. Event category tags are used to classify events into high-level categories, such as authentication, network traffic, or web activity. You can use these tags to filter and analyze events based on their category. You can learn more about event category tags from the Splunk documentation12. The other options are incorrect because they are not included with the CIM add-on. Search macros are reusable pieces of search syntax that you can invoke from other searches. They are not specific to the CIM add-on, although some Splunk apps may provide their own search macros. Workflow actions are custom links or scripts that you can run on specific fields or events. They are also not specific to the CIM add-on, although some Splunk apps may provide their own workflow actions. tsidx files are index files that store the terms and pointers to the raw data in Splunk buckets. They are part of the Splunk indexing process and have nothing to do with the CIM add-on.

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.

 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 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]

Data model datasets have a hierarchical relationship with each other, meaning they have parent-child relationships. Data models can contain multiple dataset hierarchies. There are three types of dataset hierarchies: event, search, and transaction.

https://docs.splunk.com/Splexicon:Datamodeldataset

The correct answer is C. Creates reports without using SPL. This is because the Pivot tool is a feature of Splunk that allows you to report on a specific data set without using the Splunk Search Processing Language (SPL). 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 visualizations. You can learn more about the Pivot tool from the Splunk documentation1 or watch a video tutorial2. The other options are incorrect because they do not describe the features of the Pivot tool. The Pivot tool requires data models and datasets to define the data that you want to work with. Data models and datasets are designed by the knowledge managers in your organization. You can learn more about data models and datasets from the Splunk documentation3. The Pivot tool does not create lookups, which are tables that match field values to other field values. You can create lookups using SPL or the Lookup Editor. You can learn more about lookups from the Splunk documentation.