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

The correct answer isB. 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 correct answer is D. field2 values are replaced with the value of the field1.

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 convenience1.

When you create a field alias in Splunk Web, you can select the Overwrite Field Values option to change the behavior of the field alias.This option affects how the Splunk software handles situations where the original field has no value or does not exist, as well as situations where the alias field already exists as a field in your events, alongside the original field2.

If you select the Overwrite Field Values option, the following rules apply:

If the original field does not exist or has no value in an event, the alias field is removed from that event.

If the original field and the alias field both exist in an event, the value of the alias field is replaced with the value of the original field.

If you do not select the Overwrite Field Values option, the following rules apply:

If the original field does not exist or has no value in an event, the alias field is unchanged in that event.

If the original field and the alias field both exist in an event, both fields are retained with their respective values.

Therefore, if you create a field alias where field1—field2 and select the Overwrite Field Values option, and an event only contains values for field1, then the value of field2 will be replaced with the value of field1.

References:

About calculated fields

About field aliases

Create field aliases in Splunk Web

The stats command is used to calculate summary statistics for your search results such as count, sum, avg, min, max and more2. The stats command supports various functions that you can use to perform calculations on your fields2. However, addtotals is not a stats function but a separate command that adds a row or column with the total of the values in each group2. Therefore, option B is correct, while options A, C and D are incorrect because they are valid stats functions.

The correct format for naming a macro with multiple arguments is monthly_sales3. The square brackets indicate that the macro has arguments, and the number indicates how many arguments it has. The arguments are separated by commas when calling the macro, such as monthly_sales[region,salesperson,date].

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 Fieldsand 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.

References:

About calculated fields

eval command overview

transaction command overview

[lookup command overview]

[stats command overview]

The transaction command is a Splunk command that finds transactions based on events that meet various constraints .

Transactions are made up of the raw text (the _raw field) of each member, the time and date fields of the earliest member, as well as the union of all other fields of each member .

The transaction command groups events together by matching one or more fields that have the same value across the events . For example, | transaction clientip will group events that have the same value in the clientip field.

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

https://docs.splunk.com/Documentation/Splunk/8.0.2/Knowledge/SetupaGETworkflowaction

Define a GET workflow action

Steps

Navigate to Settings > Fields > Workflow Actions.

Click New to open up a new workflow action form.

Define a Label for the action.

The Label field enables you to define the text that is displayed in either the field or event workflow menu. Labels can be static or include the value of relevant fields.

Determine whether the workflow action applies to specific fields or event types in your data.

Use Apply only to the following fields to identify one or more fields. When you identify fields, the workflow action only appears for events that have those fields, either in their event menu or field menus. If you leave it blank or enter an asterisk the action appears in menus for all fields.

Use Apply only to the following event types to identify one or more event types. If you identify an event type, the workflow action only appears in the event menus for events that belong to the event type.

For Show action in determine whether you want the action to appear in the Event menu, the Fields menus, or Both.

Set Action type to link.

In URI provide a URI for the location of the external resource that you want to send your field values to.

Similar to the Label setting, when you declare the value of a field, you use the name of the field enclosed by dollar signs.

Variables passed in GET actions via URIs are automatically URL encoded during transmission. This means you can include values that have spaces between words or punctuation characters.

Under Open link in, determine whether the workflow action displays in the current window or if it opens the link in a new window.

Set the Link method to get.

Click Save to save your workflow action definition.

The correct answer is C. When events contain unstructured data.

The regular expression method works best with unstructured event data, such as log files or text messages, where the fields are not separated by a common delimiter, such as a comma or space1.You select a sample event and highlight one or more fields to extract from that event, and the field extractor generates a regular expression that matches similar events inyour dataset and extracts the fields from them1. The regular expression method provides several tools for testing and refining the accuracy of the regular expression.It also allows you to manually edit the regular expression1.

The delimiters 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 or space1.You select a sample event, identify the delimiter, and then rename the fields that the field extractor finds1.This method is simpler and faster than the regular expression method, but it may not work well with complex or irregular data formats1.

The appendcols command in Splunk is used to connect an additional table of data directly to the right side of the existing table. It appends the results of a subsearch as new fields to the current results, effectively adding columns to the existing table.

 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 explanation is as follows:

Thetransactioncommand is used to find transactions based on events that meet various constraints12.

Transactions are made up of the raw text (the _raw field) of each member, the time and date fields of the earliest member, as well as the union of all other fields of each member1.

Thestartswithoption is used to define the first event in a transaction by specifying a search term or an expression that matches the event13.

For example,| transaction clientip JSESSIONID startswith="view"will create transactions based on theclientipandJSESSIONIDfields, and the first event in each transaction will contain the term “view” in the _raw field2.

Event types in Splunk are saved searches that categorize data, making it easier to search for specific patterns or criteria within your data. When saving an event type, the search must essentially filter events based on criteria without performing operations that transform or aggregate the data. Here's a breakdown of the options:

A. The searchindex-server_472 sourcetype-BETA_494 code-488 | stats count by codeperforms an aggregation operation (stats count by code), which makes it unsuitable for saving as an event type. Event types are meant to categorize data without aggregating or transforming it.

B. The searchindex=server_472 sourcetype=BETA_494 code=488 [ | inputlookup append=t servercode.csv]includes a subsearch and input lookup, which is typically used to enrich or filter events based on external data. This complexity goes beyond simple event categorization.

C. The searchindex=server_472 sourcetype=BETA_494 code=488 | stats where code > 200includes a filtering condition within a transforming command (stats), which again, is not suitable for defining an event type due to the transformation of data.

D. The searchindex=server_472 sourcetype=BETA_494 code-488is the correct answer as it purely filters events based on index, sourcetype, and a code field condition without transforming or aggregating the data. This is what makes it suitable for saving as an event type, as it categorizes data based on specific criteria without altering the event structure or content.

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

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

In Splunk, a field alias is used to create an alternative name for an existing field, making it easier to refer to data in a consistent manner across different searches and reports. Field aliases can be applied to both calculated fields and extracted fields. Calculated fields are those that are created using eval expressions, while extracted fields are typically those parsed from the raw data at index time or search time. This flexibility allows users to streamline their searches by using more intuitive field names without altering the underlying data. Field aliases cannot be applied to data in a lookup table, specific individual fields within a dataset, or directly to a host, source, or sourcetype.

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

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 thesearch, 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 Splunk Common Information Model (CIM) is a collection of data models that apply a common structure and naming convention to data from any source. A data model is a type of knowledge object that defines the structure and relationships of fields in a dataset. A datamodel can have one or more datasets, which are subsets of the data model that represent different aspects of the data. For example, the Network Traffic data model has datasets such as All Traffic, DNS, HTTP, etc. The CIM contains 28 pre-configured data models that cover various domains such as authentication, network traffic, web, email, etc. The CIM is implemented as an add-on that contains the JSON files for the data models, documentation, and tools that support the consistent, normalized treatment of data for maximum efficiency at search time23

1: Splunk Core Certified Power User Track, page 10. 2: Splunk Documentation, Overview of the Splunk Common Information Model 1. 3: Splunkbase, Splunk Common Information Model (CIM) 2.

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 documentation1or 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.

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

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 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.

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.

The Splunk Common Information Model (CIM) Add-on includes a variety of data models designed to normalize data from different sources to allow for cross-source reporting and analysis. Among the data models included, Alerts (Option B) and Email (Option D) are part of the CIM. The Alerts datamodel is used for data related to alerts and incidents, while the Email data model is used for data pertaining to email messages and transactions. User permissions (Option A) and Databases (Option C) are not data models included in the CIM; rather, they pertain to aspects of data access control and specific types of data sources, respectively, which are outside the scope of the CIM's predefined data models.

The Field Extractor Utility (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 has a button that displays events that do not contain extracted fields, which is the Non-Matches button2. The Non-Matches button shows you the events that do not match the regular expression that you have defined for your field extraction2. This way, you can check if your field extraction is accurate and complete2. Therefore, option B is correct, while options A, C and D are incorrect because they are not buttons that display events that do not contain extracted fields.

In Splunk, a variable for a macro is defined by placing the variable name inside dollar signs, like this: $variable name$. This syntax allows the macro to dynamically replace the variable with the appropriate value when the macro is invoked within a search. Using this method ensures that the search strings can be dynamically adjusted based on the variable's value at runtime.

References:

Splunk Docs: Use macros

Splunk Answers: Defining and Using Macros

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.

The correct answer is C. Distributable streaming. This is because the eval command is a type of command that can run on the indexers before the results are sent to the search head. This reduces the amount of data that needs to be transferred and improves the search performance. Distributable streaming commands can operate on each event or result individually, without depending on other events or results.You can learn more about the types of commands and how they affect search performance from the Splunk documentation1.

The transaction command allows you to correlate events across multiple sources. The transaction command is a search command that allows you to group events into transactions based on some common characteristics, such as fields, time, or both. A transaction is a group of events that share one or more fields that relate them to each other. A transaction can span across multiple sources or sourcetypes that have different formats or structures of data. The transaction command can help you correlate events across multiple sources by using the common fields as the basis for grouping. The transaction command can also create some additional fields for each transaction, such as duration, eventcount, startime, etc.

Using the export function, you can export search results as XML or JSON2. The export function allows you to save your search results in a structured format that can be used by other applications or tools2. You can use the output_mode parameter to specify whether you want to export your results asXML or JSON2. Therefore, options A and B are correct, while options C and D are incorrect because they are not formats that you can export your search results as.

In Splunk, data sets used in the Pivot tool are derived from data models. The Pivot tool allows users to create reports and visualizations based on the structured information available in data models.

References:

Splunk Docs - Pivot tool

The rex command allows you to extract fields from events using regular expressions. You can use the rex command to specify a named group that matches the port number in the event. For example:

rex "\+\+\+\+port (?\d+)"

This will create a field called port with the value 54 for the event.

The delimiter method is not suitable for this event because there is no consistent delimiter between the fields. The regular expression method is not a valid option for the Field Extractor tool. The Field Extractor tool can extract regular expressions, but it is not a method by itself.

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.

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.

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 Splunk Common Information Model (CIM) add-on helps you normalize your data from different sources and make it easier to analyze and report on it3. One of the functionalities that the CIM add-on relies on to normalize fields with different names is field aliases3. Field aliases allow you to assign an alternative name to an existing field without changing the original field name or value2. By using field aliases, you can map different field names from different sources or sourcetypes to a common field name that conforms to the CIM standard3. Therefore, option B is correct, while options A, C and D are incorrect.

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.

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 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 timechart command is used to create a time-series chart of statistical values based on your search results2. You can use the timechart command with a by clause to split the results by one or more fields and create multiple series in the chart2. However, you can only list one field after the by clause when using the timechart command because _time is already implied as the x-axis of the chart2. Therefore, option B is correct, while options A, C and D are incorrect.

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 tablecleaner 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 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.

Data model acceleration is a feature that speeds up searches on data models by creating and storing summaries of the data model datasets1. To enable data model acceleration, you must have administrative permissions or the accelerate_datamodel capability1. Therefore, option D is correct. Accelerated data models cannot be edited unless you disable the acceleration first1. Therefore, option B is correct. Private data models cannot be accelerated because they are not visible to other users1. Therefore, option C is correct. Root events can be accelerated as long as they are not based on a search string1. Therefore, option A is incorrect.

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.

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.

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 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.

To create a macro that accepts arguments, you must include the number of arguments in parentheses at the end of the macro name1. For example, my_macro(3) is a macro that accepts three arguments. The number of arguments in the macro name must match the number of arguments in the definition1. Therefore, option A is correct, while options B, C and D are incorrect.

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.

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.

The Field Extractor (FX) allows you to use regular expressions (regex) to extract fields from your events using a graphical interface or by manually editing the regex2. When you use the FX to perform a regex field extraction, you can use the require option to specify a string that must be present in an event for it to be included in the extraction2. This way, you can filter out events that do not contain the required string and focus on the events that are relevant for your extraction2. Therefore, option D is correct, while options A, B and C 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 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.

The relationship between data models and pivots is that data models provide the datasets for pivots. Data models are collections of datasets that represent your data in a structured and hierarchical way. Data models define how your data is organized into objects and fields. Pivots are user interfaces that allow you to create data visualizations that present different aspects of a data model. Pivots let you select options from menus and forms to create charts, tables, maps, etc., without writing any SPL code. Pivots use datasets from data models as their source of data. Pivots and data models are not the same thing, as pivots are tools for visualizing data models. Pivots do not provide datasets for data models, but rather use them as inputs.

Therefore, only statement A is true about the relationship between data models and pivots.

A 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 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 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.

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.

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.