Splunk SPLK-1002 Dumps

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.

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.

In workflow action definitions, field tokens are enclosed with dollar signs to represent field values dynamically.

Extract: “In workflow actions, enclose field names with dollar signs ($fieldname$) to insert their values dynamically into URLs or searches.”

The descriptions of the data models that come with the Splunk Common Information Model (CIM) Add-on are documented in the CIM Add-on manual (Option B). This manual provides detailed information about the data models, including their structure, the types of data they are designed to normalize, and how they can be used to facilitate cross-sourcing reporting and analysis.

The Splunk Common Information Model (CIM) provides a methodology to normalize data from different sources and source types. The CIM defines a common set of fields and tags for different types of data, such as web, network, email, etc. This allows you to search and analyze data from different sources in a consistent way.

The eval command supports a number of functions that you can use in your expressions to perform calculations, conversions, string manipulations and more2. One of the eval command functions is tostring(), which converts a numeric value to a string value2. Therefore, option D is correct, while options A, B and C are incorrect because they are not valid eval command functions.

The transaction command is 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.

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.

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.

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 search string below returns events from the data model named Application_State.

| datamodel Application_State All_Application_State search

The search string does the following:

It uses the datamodel command to access a data model in Splunk. The datamodel command takes two arguments: the name of the data model and the name of the dataset within the data model.

It specifies the name of the data model as Application_State. This is a predefined data model in Splunk that contains information about web applications.

It specifies the name of the dataset as All_Application_State. This is a root dataset in the data model that contains all events from all child datasets.

It uses the search command to filter and transform the events from the dataset. The search command can use any search criteria or command to modify the results.

Therefore, the search string returns events from the data model named Application_State.

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.

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

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

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

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

The 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.

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

The correct answers are B. duration and D. transaction id.

The explanation is as follows:

The transaction command is a Splunk command that finds 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 member12.

The transaction command adds some fields to the raw events that are part of the transaction123. These fields are:

duration: The difference, in seconds, between the timestamps for the first and last events in the transaction123.

eventcount: The number of events in the transaction123.

transaction_id: A unique identifier for each transaction3. This field is useful for filtering or joining transactions3.

Therefore, the fields that the transaction command adds to the raw events are duration and transaction_id, which are options B and D in your question.

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 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.

This search will run the third_party_outages macro and pass the arguments EMEA and -24h to the macro definition. A search macro is a reusable chunk of SPL that can be inserted into other searches. A search macro can take arguments that are used to resolve the search string at execution time. The syntax for using a search macro is macro_name (argument1, argument2, ...).ReferencesSee Use search macros in searches and Search macro examples in the Splunk Documentation.

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.

In Splunk, you can create an event type from the search window by running a search that would make a good event type, then clicking Save As and selecting Event Type1. This opens the Save as Event Type dialog, where you can provide the event type name and optionally apply tags to it1.

You can also create an event type by editing the eventtypes.conf file and adding a new stanza1. Each stanza in the eventtypes.conf file represents an event type1. The stanza name is the name of the event type, and the search attribute specifies the search string that defines the event type1.

It’s important to note that while you can use the eventtype command in a search to find events associated with a specific event type, adding | eventtype to the SPL and executing the search does not create a new event type1. Similarly, clicking Event Actions > Build Event Type in an event’s detail dropdown does not create a new event type1.

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.

To retrieve data from an external resource based on a field like Zip_Code, a GET workflow action is appropriate. GET requests retrieve information by passing the Zip_Code as a parameter to an external URL, commonly used in weather APIs.

The maxspan option in the transaction command limits the maximum time span of the transaction to the specified duration, in this case, 5 minutes. This ensures that only events within that time window are grouped into one transaction.

The transaction command in Splunk groups multiple related events into a single logical transaction based on shared field values (such as session_id or user).

Extract: “The transaction command groups events that share common field values into a single event that represents a transaction.”

Option D accurately describes this purpose; options B and C refer to system-level data transfers, which are unrelated to transaction.

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 C. By enclosing the macro name in backtick characters (`).

A macro is a way to reuse a piece of SPL code in different searches. A macro can 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 reference a macro in a search, you need to enclose the macro name in backtick characters (). For example, if you have a macro named my_macro` that takes one argument, you can reference it in a search by using the following syntax:

| my_macro(argument) | ...

This will replace the macro name and argument with the SPL code contained in the macro definition. For example, if the macro definition is:

[my_macro(argument)] search sourcetype=$argument$

And you reference it in a search with:

index=main | my_macro(web) | stats count by host

This will expand the macro and run the following SPL code:

index=main | search sourcetype=web | stats count by host

The Splunk Common Information Model (CIM) Add-on is a foundational component for many Splunk apps, providing a common framework for data normalization and field extraction. This add-on includes a set of pre-configured data models that are essential for consistent reporting, searching, and correlation across various types of data. These data models help standardize field names and event structures, ensuring that data from disparate sources can be queried in a uniform way. While the CIM Add-on facilitates the use of standardized sourcetypes and supports data validation, the primary feature it offers is the set of pre-configured data models which are crucial for maintaining consistency across different datasets.

In Splunk, macros that accept arguments are defined with placeholders for those arguments in the format example(var1, var2). In the search example(100,200), "100" and "200" are the values passed for var1 and var2 respectively.

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.

Splunk ships with the following KMZ files for choropleth maps: States of the United States and Countries of the World. A KMZ file is a compressed file that contains a KML file and other resources. A KML file is an XML file that defines geographic features and their properties. A KMZ file can be used to create choropleth maps in Splunk by using the geom command. A choropleth map is a type of map that shows geographic regions with different colors based on some metric. Splunk ships with two KMZ files that define the geographic regions for choropleth maps:

States of the United States: This KMZ file defines the 50 states of the United States and their boundaries. The name of this KMZ file is us_states.kmz and it is located in the $SPLUNK_HOME/etc/apps/maps/appserver/static/geo directory.

Countries of the World: This KMZ file defines the countries of the world and their boundaries. The name of this KMZ file is world_countries.kmz and it is located in the $SPLUNK_HOME/etc/apps/maps/appserver/static/geo directory.

Splunk does not ship with KMZ files for States and provinces of the United States and Canada or Countries of the European Union. However, you can create your own KMZ files or download them from external sources and use them in Splunk.

Search macros with arguments must include the number of arguments in parentheses.

Extract: “If your macro includes arguments, append the number of arguments to the macro name. For example, mymacro(2).”

Thus, samplemacro(2) is correct.

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

The transaction command in Splunk groups events based on common fields and creates a single transaction event. The timestamp assigned to the transaction is the timestamp of the earliest event in the transaction. This ensures that the transaction reflects the actual start time of the related events rather than the time the transaction is created or ended.

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.

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

A field alias is a way to associate an additional (new) name with an existing field name. A field alias can be used to normalize fields from different sources that have different names but represent the same data. Field aliases can also be used to rename fields for clarity or convenience1.

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

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

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

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

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

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

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

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

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.

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

When performing an outer join in Splunk using the | join employeeNumber type=outer command, it combines the rows from both tables based on the employeeNumber field. An outer join returns all rows from both tables, with matching rows from both sides where available. If there is no match, the result is NULL on the side of the join where there is no match.

In the provided tables, there are five rows in the first table and three in the second. Since it’s an outer join, all rows from both tables will be returned. This means the new table will have a total of eight rows, combining the matched rows and the unmatched rows from both tables.

The time range specified for a historical search defines the amount of data fetched from the index matching that time range2. A historical search is a search that runs over a fixed period of time in the past2. When you run a historical search, Splunk searches the index for events that match your search string and fall within the specified time range2. Therefore, option B is correct, while options A and C are incorrect because they are not what the time range defines for a historical search.

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.

In Splunk, a workflow action that returns an external IP lookup for a field named domain would typically use the GET method. This HTTP method is used to retrieve data from a specified resource, which is appropriate for looking up information based on the domain field.

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.

 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.

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 .

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.

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.

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.

GET workflow actions are custom actions that open a URL link when you click on a field value in your search results. GET workflow actions can be configured with various options, such as label name, base URL, URI parameters, app context, etc. One of the options is to choose whether to open the URL link in the current window or in a new window. GET workflow actions do not have to be configured with POST arguments, as they use GET method to send requests to web servers. Configuration of GET workflow actions does not include choosing a sourcetype, as they do not generate any data in Splunk. Label names for GET workflow actions must include a field name surrounded by dollar signs, as this indicates the field value that will be used to replace the variable in the URL link.

 The 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.

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 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.

Field aliases are applied based on host, source, or sourcetype.

Extract: “Create field aliases by specifying the host, source, or sourcetype that the alias applies to.”

Thus, the correct answer is Sourcetypes.

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

The explanation is as follows:

Search macros are knowledge objects that allow you to insert chunks of SPL into other searches12.

Search macros can be any part of a search, such as an eval statement or a search term, and do not need to be a complete command12.

You can also specify whether the macro field takes any arguments and define validation expressions for them12.

Search macros can help you make your SPL searches shorter and easier to understand3.

To use a search macro in a search string, you need to put a backtick character () before and after the macro name[^1^][1]. For example, mymacro`.

In Splunk, a macro can accept arguments, and the correct syntax for a macro that takes two arguments is macro_name(argument1, argument2). In this case, the macro is called samplemacro, and it accepts two arguments, so the correct format would be samplemacro(1,2). This syntax allows for passing dynamic values into the macro, which can then be used to modify the search dynamically based on the arguments provided.

Splunk alerts can be based on searches that run in real-time or on a regular schedule3. An alert is a way to monitor your data and get notified when certain conditions are met3. You can create an alert by specifying a search and a triggering condition3. You can also specify how often you want to run the search and how you want to receive the alert notifications3. You can run the alert search in real-time, which means that it continuously monitors your data as it streams into Splunk3. Alternatively, you can run the alert search on a regular schedule, which means that it runs at fixed intervals such as every hour or every day3. Therefore, options A and B are correct, while option C is incorrect because it is not a way to run an alert search.

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.

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.

When creating an event type in Splunk, subsearches are allowed in the search string. Subsearches enable users to perform a secondary search whose results are used as input for the main search. This functionality is useful for more complex event type definitions that require additional filtering or criteria based on another search.

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 Splunk Common Information Model (CIM) is an app that contains a set of predefined data models that apply a common structure and naming convention to data from any source. The CIM enables you to use data from different sources in a consistent and coherent way. The CIM contains 28 pre-configured datasets that cover various domains such as authentication, network traffic, web, email, etc. The data models included in the CIM are configured with data model acceleration turned on by default, which means that they are optimized for faster searches and analysis. Data model acceleration creates and maintains summary data for the data models, which reduces the amount of raw data that needs to be scanned when you run a search using a data model.

Splunk Core Certified Power User Track, page 10. : Splunk Documentation, About the Splunk Common Information Model.

The eval command function tostring() is valid. The tostring() function converts a numeric value to a string value. For example, tostring(3.14) returns “3.14”. The other functions are not valid eval command functions.

When using the Field Extractor (FX) in Splunk to perform field extraction, any consistent character can be used as a delimiter. The Field Extractor allows users to define how fields are separated in the raw event data, and as long as the delimiter is consistent, the FX tool can parse and extract the fields correctly.

An event type is a classification of events based on a search query, which allows for a static set of search criteria. In this case, option A (index=server_48 sourcetype=BETA_881 code=220) represents a simple search without transforming commands (e.g., stats, inputlookup). Event types cannot include transforming commands such as stats or lookup.References:Splunk Documentation - Event Types

When using the Field Extractor (FX) in Splunk for regex field extraction, it's important to select the context in which you want to perform the extraction. The context is essentially the subset of data you're focusing on for your field extraction task.

D. Sourcetype or source: This is the correct option. In the initial steps of using the Field Extractor tool, you're prompted to choose a data type for your field extraction. The options available are typically based on the nature of your data and how it's organized in Splunk. "Sourcetype" refers to the kind of data you're dealing with, a categorization that helps Splunk apply specific processing rules. "Source" refers to the origin of the data, like a specific log file or data input. By selecting either a sourcetype or source, you're narrowing down the dataset on which you'll perform the regex extraction, making it more manageable and relevant.

Macros in Splunk are defined with the number of arguments inside parentheses after the macro name. For two arguments, the syntax is samplemacro(2). This defines a macro accepting two arguments.

The delimiter method in the Field Extractor should be used when fields in events are separated consistently by a specific character (such as a comma, tab, or pipe). This method simplifies extraction without requiring complex regular expressions.

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.

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.

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.

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.