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

 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.

 There are several ways to access the field extractor. The option that automatically identifies data type, source type, and sample event is Fields sidebar > Extract New Field. The field extractor is a tool that helps you extract fields from your data using delimiters or regular expressions. The field extractor can generate a regex for you based on your selection of sample values or you can enter your own regex in the field extractor. The field extractor can be accessed by using various methods, such as:

• Fields sidebar > Extract New Field: This is the easiest way to access the field extractor. The fields sidebar is a panel that shows all available fields for your data and their values. When you click on Extract New Field in the fields sidebar, Splunk will automatically identify the data type, source type, and sample event for your data based on your current search criteria. You can then use the field extractor to select sample values and generate a regex for your new field.
• Event Actions > Extract Fields: This is another way to access the field extractor. Event actions are actions that you can perform on individual events in your search results, such as viewing event details, adding to report, adding to dashboard, etc. When you click on Extract Fields in the event actions menu, Splunk will use the current event as the sample event for your data and ask you to select the source type and data type for your data. You can then use the field extractor to select sample values and generate a regex for your new field.
• Settings > Field Extractions > New Field Extraction: This is a more advanced way to access the field extractor. Settings is a menu that allows you to configure various aspects of Splunk, such as indexes, inputs, outputs, users, roles, apps, etc. When you click on New Field Extraction in the Settings menu, Splunk will ask you to enter all the details for your new field extraction manually, such as app context, name, source type, data type, sample event, regex, etc. You can then use the field extractor to verify or modify your regex for your new field.

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 searches A and C show a valid use of a macro. A macro is a reusable piece of SPL code that can be called by using single quotes (‘’). A macro can take arguments, which are passed inside parentheses after the macro name. For example, ‘makeMyField(oldField)’ calls a macro named makeMyField with an argument oldField. The searches B and D are not valid because they use double quotes (“”) instead of single quotes (‘’).

The explanation is as follows:

• The transaction command 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.
• The startswith option 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 the clientip and JSESSIONID fields, and the first event in each transaction will contain the term “view” in the _raw field2.

Normalize your data for each of these fields using a combination of field aliases, field extractions, and lookups.

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 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

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.

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.

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.

"Calculated fields can reference all types of field extractions and field aliasing, but they cannot reference lookups, event types, or tags."

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]

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. eventtype=web_errors.

An event type is a way to categorize 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 reports1.

To search for events that have a specific event type, you need to use the eventtype field with the name of the event type as the value. The syntax for this is:

eventtype=

For example, if you want to search for events that have the event type web_errors, you can use the following syntax:

eventtype=web_errors

This will return only the events that match the search criteria defined by the web_errors event type.

The other options are not correct because they use different syntax or fields that are not related to event types. These options are:

• A. tag=web_errors: This option uses the tag field, which is a way to add descriptive keywords to events based on field values. Tags are different from event types, although they can be used together. Tags can be used to filter and group events by common characteristics2.
• C. eventtype “web errors”: This option uses quotation marks around the event type name, which is not valid syntax for the eventtype field. Quotation marks are used to enclose phrases or exact matches in a search3.
• D. eventtype (web_errors): This option uses parentheses around the event type name, which is also not valid syntax for the eventtype field. Parentheses are used to group expressions or terms in a search3.

References:

• About event types
• About tags
• Search command cheatsheet

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 macro is a way to save a segment of a search string as a variable and reuse it in other searches2. A macro can be followed by a pipe, which is a symbol that separates commands in a search pipeline2. A pipe may always follow a macro, regardless of who owns the macro, where the macro is defined or how the macro is shared2. For example, if you have a macro called us_sales that returns events from the US region, you can use it in a search like this: us_sales | stats sum(price) by product2. This search will use the macro to filter the events and then calculate the total price for each product2. Therefore, option A is correct, while options B, C and D are incorrect because they are not conditions that affect whether a pipe can follow a macro.

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 B. Root event dataset. This is because root event datasets are defined by a constraint that filters out events that are not relevant to the dataset. A constraint for a root event dataset is a simple search that returns a fairly wide range of data, such as sourcetype=access_combined. Without a constraint, a root event dataset would include all the events in the index, which is not useful for data modeling. You can learn more about how to design data models and add root event datasets from the Splunk documentation1. The other options are incorrect because root transaction datasets and root search datasets have different ways of defining their datasets, such as transaction definitions or complex searches, and root child datasets are not a valid type of root dataset.

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.

During the validation step of the Field Extractor workflow, you can remove values that aren’t a match for the field you want to define2. The validation step allows you to review and edit the values that have been extracted by the FX and make sure they are correct and consistent2. You can remove values that aren’t a match by clicking on them and selecting Remove Value from the menu2. This will exclude them from your field extraction and update the regular expression accordingly2. Therefore, option A is correct, while options B and C are incorrect because they are not actions that you can perform during the validation step of the Field Extractor workflow.

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 data model 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.

When defining a search macro, the required elements are the name and the definition of the macro. The name is a unique identifier for the macro that can be used to invoke it in other searches. The definition is the search string that the macro expands to when referenced. The arguments, validation expression, and validation error message are optional elements that can be used to customize the macro behavior and input validation2

1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, Define search macros in Settings.

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

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

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

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

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

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

• B. When a log file contains empty lines or comments: This option does not indicate that the log file has a structured format or a common delimiter. The delimited method might not work well with this type of data, as it might miss some fields or include some unwanted values.
• C. With structured files such as JSON or XML: This option does not require the delimited method, as Splunk can automatically extract fields from JSON or XML files by using indexed extractions or search-time extractions2. The delimited method might not work well with this type of data, as it might not recognize the nested structure or the special characters.
• D. When the file has a header that might provide information about its structure or format: This option does not indicate that the file has a common delimiter between the fields. The delimited method might not work well with this type of data, as it might not be able to identify the fields based on the header information.

References:

• Build field extractions with the field extractor
• Configure indexed field extraction

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 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.

The fillnull command 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. The fillnull command 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=0 This command would replace all null values in the search results with 0.

In Splunk, when using the chart command, the useother parameter can be set to false (f) to remove the 'OTHER' category, which is a bucket that Splunk uses to aggregate low-cardinality groups into a single group to simplify visualization. Here's how the options break down:

A. | chart count over CurrentStanding by Action useother=fThis command correctly sets the useother parameter to false, which would prevent the 'OTHER' category from being displayed in the resulting visualization.

B. | chart count over CurrentStanding by Action usenull=f useother=tThis command has useother set to true (t), which means the 'OTHER' category would still be included, so this is not a correct option.

C. | chart count over CurrentStanding by Action limit=10 useother=fSimilar to option A, this command also sets useother to false, additionally imposing a limit to the top 10 results, which is a way to control the granularity of the chart but also to remove the 'OTHER' category.

D. | chart count over CurrentStanding by Action limit-10This command has a syntax error (limit-10 should be limit=10) and does not include the useother=f clause. Therefore, it would not remove the 'OTHER' category, making it incorrect.

The correct answers to rewrite the syntax to remove the 'OTHER' category are options A and C, which explicitly set useother=f.

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.

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.

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.

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 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 the cumulative 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.

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 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.

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.

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.

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.

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.

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.

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.

 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 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 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.

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.

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.

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 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.