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.

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

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

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

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

Data model fields are fields that describe the attributes of a dataset in a data model2. Data model fields can be added using various methods such as Auto-Extracted, Evaluated or Lookup2. Auto-Extracted fields are fields that are automatically extracted from your raw data using various techniques such as regular expressions, delimiters or key-value pairs2. Auto-Extracted fields can be hidden in Pivot, which means that you can choose whether to display them or not in the Pivot interface2. Therefore, option A is correct. Auto-Extracted fields can have their data type changed, which means that you can specify whether they are strings, numbers, booleans or timestamps2. Therefore, option B is correct. Auto-Extracted fields can be given a friendly name for use in Pivot, which means that you can assign an alternative name to them that is more descriptive or user-friendly than the original field name2. Therefore, option C is correct. Auto-Extracted fields can be added if they already exist in the dataset with constraints, which means that you can include them in your data model even if they are already extracted from your raw data by applying filters or constraints to limit the scope of your dataset2. Therefore, option D is correct.

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

The 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.

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.

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

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

A chart is a graphical representation of your search results that shows the relationship between two or more fields2. You can display a chart in stack mode by changing the Stack Mode option in the Format menu2. Stack mode allows you to stack multiple series on top of each other in a chart to show 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 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 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 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.

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.

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.

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

The Splunk Common Information Model (CIM) add-on is a Splunk app that contains data models to help you normalize data from different sources and formats. The CIM add-on defines a common and consistent way of naming and categorizing fields and events in Splunk. This makes it easier to correlate and analyze data across different domains, such as network, security, web, etc. The CIM add-on does not use machine learning to normalize data, but rather relies on predefined field names and values. The CIM add-on does not contain dashboards that show how to map data, but rather provides documentation and examples on how to use the data models. The CIM add-on is not automatically installed in a Splunk environment, but rather needs to be downloaded and installed from Splunkbase.

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.

• The eval command is a Splunk command that allows you to create or modify fields using expressions .
• The if function is an expression that evaluates a condition and returns a value based on whether the condition is true or false. The syntax of the if function is if(X,Y,Z), where X is the condition, Y is the value to return if X is true, and Z is the value to return if X is false.
• The isnotnull function is an expression that returns true if the argument is not null, and false otherwise. The syntax of the isnotnull function is isnotnull(X), where X is the argument to check.
• Therefore, the expression if (isnotnull (src), src, host) returns the value of src if it is not null, and the value of host otherwise. This means that it will provide a new value for host from src if it exists, and keep the original value of host otherwise.

By default, acceleration is determined automatically based on the data source in the Splunk Common Information Model (CIM) add-on. The Splunk CIM Add-on is an app that provides common data models for various domains, such as network traffic, web activity, authentication, etc. The CIM Add-on allows you to normalize and enrich your data using predefined fields and tags. The CIM Add-on also allows you to accelerate your data models for faster searches and reports. Acceleration is a feature that pre-computes summary data for your data models and stores them in tsidx files. Acceleration can improve the performance and efficiency of your searches and reports that use data models.

By default, acceleration is determined automatically based on the data source in the CIM Add-on. This means that Splunk will decide whether to enable or disable acceleration for each data model based on some factors, such as data volume, data type, data model complexity, etc. However, you can also manually enable or disable acceleration for each data model by using the Settings menu or by editing the datamodels.conf file.

A data model that has been accelerated can be used with Pivot, the | tstats command, or the | datamodel command (Option A). Acceleration pre-computes and stores results for quicker access, enhancing the performance of searches and analyses that utilize the data model, especially for large datasets. This makes accelerated data models highly efficient for use in various analytical tools and commands within Splunk.

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

Tags are a type of knowledge object that enable you to assign descriptive keywords to events. Tags can reference any of the following knowledge objects: extracted fields, field aliases, calculated fields, lookups, and event types. Tags cannot reference other tags or search macros. Tags are applied to events at search time based on the values of the fields that they reference2

1: Splunk Core Certified Power User Track, page 10. 2: Splunk Documentation, About tags and aliases.

When you define a calculated field, you can specify the name of the field that the eval expression will create or modify. If the name of the calculated field matches the name of an existing extracted field, the calculated field will override the extracted field and replace its value with the result of the eval expression. This means that the original value of the extracted field will not be available for searching or analysis. To avoid this, you should use a unique name for your calculated field or use a different name for your extracted field2

1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, Configure calculated fields with props.conf.

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.

• Tags are a knowledge object that allow you to assign an alias to one or more field values . Tags are applied to events at search time and can be used as search terms or filters .
• Tags can help you make your data more understandable by replacing cryptic or complex field values with meaningful names . For example, you can tag the value 200 in the status field as success, or tag the value 404 as not_found .

The tag=Priv* search will return events containing a tag named Privileged, as well as any other tag that starts with Priv. The asterisk (*) is a wildcard character that matches zero or more characters. The other searches will not match the exact tag name.

When you mouse over and click to add a search term from the Fields sidebar or from an event in your search results, Splunk automatically adds the term to your search string with an implied AND operator2. However, this does not apply to some Boolean operators such as OR, NOT and parentheses (). These operators are not implied when you add a search term and you have to type them manually if you want to use them in your search string2. Therefore, options A, B and D are correct, while option C is incorrect because AND is implied when you add a search term.

The fields sidebar is a panel that shows the fields that are present in your search results2. The fields sidebar does not show all extracted fields, which are fields that are extracted from your raw data using various methods such as regular expressions, delimiters or key-value pairs2. The fields sidebar only shows selected fields and interesting fields2. Selected fields are fields that you choose to display in your search results by clicking on them in the fields sidebar or by using the fields command2. Interesting fields are fields that appear in at least 20 percent of events or have high variability among values2. Therefore, option C is correct, while options A and B are incorrect because they are types of fields that the fields sidebar does show.

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 correct answer is B. Transforming.

The explanation is as follows:

• The timechart command is a Splunk command that creates a time series chart with corresponding table of statistics12.
• A timechart is a statistical aggregation applied to a field to produce a chart, with time used as the X-axis1. You can specify a split-by field, where each distinct value of the split-by field becomes a series in the chart1.
• Transforming commands are commands that change the format of the search results into a data structure that can be easily visualized3. Transforming commands often use stats functions to aggregate and summarize data3.
• Therefore, the timechart command is an example of a transforming command, as it transforms the search results into a chart and a table using stats functions123.

A Search workflow action is the appropriate choice when a user wants to retrieve a specific field value from an event and run a search in a new browser window within their Splunk instance (Option B). This type of workflow action allows users to define a search that utilizes field values from selected events as parameters, enabling more detailed investigation or context-specific analysis based on the original search results.

In Splunk, the transaction command is used to group events that share common characteristics into a single transaction1. By default, the transaction command groups all matching events into a single transaction1.

However, you can use the maxspan option to limit the time span of the transactions1. If the time span between the first and last event in a transaction exceeds the maxspan value, the transaction command will start a new transaction1.

Therefore, if the maxspan option is not included in the search, the transaction command might produce multiple transactions instead of one if the time span between the first and last event in a transaction exceeds the default maxspan value1.

Here is an example of how you can use the maxspan option in a search:

index=main sourcetype=access_combined | transaction someuniqefield maxspan=1h

In this search, the transaction command groups events that share the same someuniqefield value into a single transaction, but only if the time span between the first and last event in the transaction does not exceed 1 hour1. If the time span exceeds 1 hour, the transaction command will start a new transaction1.

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.

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 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 (‘’).

When using the Field Extractor (FX) tool in Splunk and the tool fails to extract a value from all appropriate events, there are specific steps you can take to improve the extraction process. These steps involve interacting with the FX tool and possibly adjusting the extraction method:

A. Select an additional sample event with the Field Extractor (FX) and highlight the missing value in the event. This approach allows Splunk to understand the pattern better by providing more examples. By highlighting the value in another event where it wasn't extracted, you help the FX tool to learn the variability in the data format or structure, improving the accuracy of the field extraction.

D. Edit the regular expression manually. Sometimes the FX tool might not generate the most accurate regular expression for the field extraction, especially when dealing with complex log formats or subtle nuances in the data. In such cases, manually editing the regular expression can significantly improve the extraction process. This involves understanding regular expression syntax and how Splunk extracts fields, allowing for a more tailored approach to field extraction that accounts for variations in the data that the automatic process might miss.

Options B and C are not typically related to improving field extraction within the Field Extractor tool. Re-ingesting data (B) does not directly impact the extraction process, and changing to a delimited extraction method (C) is not always applicable, as it depends on the specific data format and might not resolve the issue of missing values across events.

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.

The correct answer is C. Events where values of field A are equal to values of field B.

The where command is used to filter the search results based on an expression that evaluates to true or false. The where command can compare two fields, two values, or a field and a value. The where command can also use functions, operators, and wildcards to create complex expressions1.

The syntax for the where command is:

| where

The expression can be a comparison, a calculation, a logical operation, or a combination of these. The expression must evaluate to true or false for each event.

To compare two fields with the where command, you need to use the field names without any quotation marks. For example, if you want to find events where the values for the field A match the values for the field B, you can use the following syntax:

| where A=B

This will return only the events where the two fields have the same value.

The other options are not correct because they use different syntax or fields that are not related to the where command. These options are:

• A. Events that contain the string value where A=B: This option uses the string value where A=B as a search term, which is not valid syntax for the where command. This option will return events that have the literal text “where A=B” in them.
• B. Events that contain the string value A=B: This option uses the string value A=B as a search term, which is not valid syntax for the where command. This option will return events that have the literal text “A=B” in them.
• D. Events where field A contains the string value B: This option uses quotation marks around the value B, which is not valid syntax for comparing fields with the where command. Quotation marks are used to enclose phrases or exact matches in a search2. This option will return events where the field A contains the string value “B”.

References:

• where command usage
• Search command cheatsheet

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

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

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