SPLK-1002 Questions and Answers

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 correct answer is D. eval.

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

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

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

| eval total=price+tax

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

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

• A. transaction: This command is used to group events that share some common values into a single record, called a transaction. A transaction can span multiple events and multiple sources, and can be useful for correlating events that are related but not contiguous3.
• B. lookup: This command is used to enrich events with additional fields from an external source, such as a CSV file or a database. A lookup can add fields to events based on the values of existing fields, such as host, source, sourcetype, or any other extracted field.
• C. stats: This command is used to calculate summary statistics on the fields in the search results, such as count, sum, average, etc. It can be used to group and aggregate data by one or more fields.

References:

• About calculated fields
• eval command overview
• transaction command overview
• [lookup command overview]
• [stats command overview]

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 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.

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 data model 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 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.

Arguments are defined within the macro search string by using dollar signs on either side of the argument name, such as arg1 or fragment.

References

Search macro examples

Define search macros in Settings

Use search macros in searches

The correct answer is C. ‘weekly_sales (3.99, 10)’. This is because search macros accept arguments without quotation marks or dollar signs, and the number of arguments must match the number of parameters defined in the macro. The other options are incorrect because they either use quotation marks or dollar signs around the arguments, or they provide a different number of arguments than the macro expects. You can learn more about how to use search macros in searches from the Splunk documentation1.

The transaction command is used to group events that are related by some common fields or conditions, such as start/end values, time span, or pauses. The stats command is used to calculate statistics on a group of events by a common field value.

References

Splunk Community

Splunk Transaction - Exact Details You Need

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

References

CIM Add-on manual

Splunk Common Information Model (CIM) | Splunkbase

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

Hello, this is Bing. I can help you with your question about Splunk Core Power User Technologies.

The correct answer is D. Event types do not include a time range.

The explanation is as follows:

• Event types are a categorization system that help you make sense of your data by matching events with the same search string1. Event types are applied to events at search time and can be used as search terms or filters12.
• Saved reports are results saved from a search action that can show statistics and visualizations of events3. Saved reports can be run anytime, and they fetch fresh results each time they are run34. Saved reports can be shared with other users and added to dashboards4.
• The main difference between event types and saved reports is that event types do not include a time range, while saved reports do14. This means that event types can match events from any time period, while saved reports are limited by the time range specified when they are created or run14.

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 search index-server_472 sourcetype-BETA_494 code-488 | stats count by code performs 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 search index=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 search index=server_472 sourcetype=BETA_494 code=488 | stats where code > 200 includes 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 search index=server_472 sourcetype=BETA_494 code-488 is 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.

A report that is scheduled to run every 15 minutes but takes 17 minutes to complete is in danger of being skipped or deferred2. This means that Splunk may skip some scheduled runs of the report if they overlap with previous runs that are still in progress or defer them until the previous runs are finished2. This can affect the accuracy and timeliness of the report results and notifications2. Therefore, option A is correct, while options B, C and D are incorrect because they are not consequences of a report taking longer than its schedule interval.

Field aliases in Splunk are alternate names assigned to fields. These can be particularly useful for normalizing data from different sources or simply for making field names more intuitive. Once an alias is created for a field, it can be used across various Splunk knowledge objects, enhancing their flexibility and utility.

A. Calculated fields, lookups, event types, and tags: This is the correct answer. Field aliases can indeed be referenced in calculated fields, lookups, event types, and tags within Splunk. When you create an alias for a field, that alias can then be used in these knowledge objects just like any standard field name.

• Calculated fields: These are expressions that can create new field values based on existing data. You can use an alias in a calculated field expression to refer to the original field.
• Lookups: These are used to enrich your event data by referencing external data sources. If you've created an alias for a field that matches a field in your lookup table, you can use that alias in your lookup configurations.
• Event types: These are classifications for events that meet certain search criteria. You can use field aliases in the search criteria for defining an event type.
• Tags: These allow you to assign meaningful labels to data, making it easier to search and report on. You can use field aliases in the search criteria that you tag.

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

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

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

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

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 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 in your 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.

This is because event types are added to events as a field named eventtype, and you can use this field as a search term to find events that match a specific event type. For example, eventtype=successful_purchases returns all events that have been categorized as successful purchases by the event type definition. The other options are incorrect because they either use a different field name (tag), a different syntax (Event Type:: or event type—), or have a typo (success ful_purchases). You can learn more about how to use event types in searches from the Splunk documentation1.

The correct syntax to find events associated with a tag in Splunk is tag=1. So, the correct answer is D. tag=. This syntax allows you to annotate specified fields in your search results with tags1.

In Splunk, tags are a type of knowledge object that you can use to add meaningful aliases to field values in your data1. For example, if you have a field called status_code in your data, you might have different status codes like 200, 404, 500, etc. You can create tags for these status codes like success for 200, not_found for 404, and server_error for 500. Then, you can use the tag command in your searches to find events associated with these tags1.

Here is an example of how you can use the tag command in a search:

index=main sourcetype=access_combined | tag status_code

In this search, the tag command annotates the status_code field in the search results with the corresponding tags. If you have tagged the status code 200 with success, the status code 404 with not_found, and the status code 500 with server_error, the search results will include these tags1.

You can also use the tag command with a specific tag value to find events associated with that tag. For example, the following search finds all events where the status code is tagged with success:

index=main sourcetype=access_combined | tag status_code | search tag::status_code=success

In this search, the tag command annotates the status_code field with the corresponding tags, and the search command filters the results to include only events where the status_code field is tagged with success1.

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]

https://docs.splunk.com/Documentation/Splunk/8.1.1/SearchReference/ConditionalFunctions

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

To create a tag, the user must have the tag capability associated with their user role. The tag capability allows the user to create, edit, and delete tags. The user does not need to identify a field:value pair, have the Power role, or be able to edit the sourcetype the tag applies to.ReferencesSee Define and manage tags in Settings and [About capabilities] in the Splunk Documentation.

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.

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 as XML 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.

The where command can be used to complete the search below.

… | where failure>successes

The where command is a search command that allows you to filter events based on complex or custom criteria. The where command can use any boolean expression or function to evaluate each event and determine whether to keep it or discard it. The where command can also compare fields or perform calculations on fields using operators such as >, <, =, +, -, etc. The where command can be used after any transforming command that creates a table or a chart.

The search string below does the following:

• It uses … to represent any search criteria or commands before the where command.
• It uses the where command to filter events based on a comparison between two fields: failure and successes.
• It uses the greater than operator (>) to compare the values of failure and successes fields for each event.
• It only keeps events where failure is greater than successes.

 To configure a Search Workflow Action to run at the same time range as the original search, you need to select the “Use the same time range as the search that created the field listing” checkbox. This will ensure that the workflow action search uses the same earliest and latest time parameters as the original search.

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 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.

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.

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.

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.

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 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.

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.

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

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.

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.

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.

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.

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.

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 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.

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.

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 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.