Free and Premium Splunk SPLK-1002 Dumps Questions Answers

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 maxpause option of the transaction command in Splunk is used to specify the maximum time allowed between events in a transaction. If the time between events exceeds the maxpause value, those events are not considered part of the same transaction.

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.

To search for a tag on a specific field, Splunk requires the syntax tag::=.

Extract: “To search for a tag associated with a field, use tag::=.”

Thus, tag::host=prod is correct.

In Splunk, the NOT operator is used to exclude events from your search results. The search index=network NOT StatusCode=200 will return all events in the ‘network’ index where the StatusCode is not 200. This includes events where the StatusCode field is present and has a value other than 200, as well as events where the StatusCode field is not present at all.

The correct answer is B. Event category tags. This is because the CIM add-on contains a collection of preconfigured data models that you can apply to your data at search time. Each data model in the CIM consists of a set of field names and tags that define the least common denominator of a domain of interest. Event category tags are used to classify events into high-level categories, such as authentication, network traffic, or web activity. You can use these tags to filter and analyze events based on their category. You can learn more about event category tags from the Splunk documentation12. The other options are incorrect because they are not included with the CIM add-on. Search macros are reusable pieces of search syntax that you can invoke from other searches. They are not specific to the CIM add-on, although some Splunk apps may provide their own search macros. Workflow actions are custom links or scripts that you can run on specific fields or events. They are also not specific to the CIM add-on, although some Splunk apps may provide their own workflow actions. tsidx files are index files that store the terms and pointers to the raw data in Splunk buckets. They are part of the Splunk indexing process and have nothing to do with the CIM add-on.

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

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 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 multiple expressions in a single eval command in Splunk, the delimiter used is a comma (,). This allows for the execution of multiple operations within a single eval statement, separating each operation clearly.

transaction groups related events and preserves raw event data.

Extract: “Transactions contain the raw text (_raw) of each member event, earliest time fields, and all other field values.”

Thus, unlike stats, the transaction command retains the original raw data for analysis.

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.

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 chart command with sum(price) by product, region will return a table where the total revenue (price) is aggregated (sum) for each product and sales region. This is the correct way to aggregate data in Splunk.

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.

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.

If a search returns statistics, it can be viewed as a chart2. Statistics are tabular data that show the relationship between two or more fields2. You can create statistics by using commands such as stats, chart or timechart2. You can view statistics as a chart by selecting the Visualization tab in the Search app and choosing a chart type such as column, line or pie2. Therefore, option B is correct, while options A, C and D are incorrect because they are not types of data that can be viewed as a chart.

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.

In Splunk, the order of operations for Boolean logic in the where command follows this sequence:

Parentheses: Operations inside parentheses are evaluated first.

NOT: The NOT operator is evaluated after parentheses.

AND: The AND operator is evaluated next.

OR: Finally, the OR operator is evaluated last.

This order ensures that expressions within parentheses are given priority, followed by negations (NOT), conjunctions (AND), and finally disjunctions (OR).

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.

One of the valid options to speed up reports is to edit acceleration, which means that you can enable summary indexing or data model acceleration for your reports to improve their performance2. Summary indexing allows you to create reports that run over large amounts of data by storing the results of scheduled searches in a summary index and using that index for faster reporting2. Data model acceleration allows you to create reports that use data models by creating and storing summaries of the data model datasets and using them for faster reporting2. Therefore, option C is correct, while options A, B and D are incorrect because they are not options to speed up reports.

The 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.

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.

The correct answer is B. The value for the productName field because it appears first.

The coalesce function is an eval function that takes an arbitrary number of arguments and returns the first value that is not null. A null value means that the field has no value at all, while an empty value means that the field has a value, but it is “” or zero-length1.

The coalesce function can be used to combine fields that have different names but represent the same data, such as IP address or user name. The coalesce function can also be used to rename fields for clarity or convenience2.

The syntax for the coalesce function is:

coalesce(,,…)

The coalesce function will return the value of the first field that is not null in the argument list. If all fields are null, the coalesce function will return null.

For example, if you have a set of events where the IP address is extracted to either clientip or ipaddress, you can use the coalesce function to define a new field called ip, that takes the value of either clientip or ipaddress, depending on which is not null:

| eval ip=coalesce(clientip,ipaddress)

In your example, you have a set of events where the product name is extracted to either productName or productid, and you use the coalesce function to define a new field called productINFO, that takes the value of either productName or productid, depending on which is not null:

| eval productINFO=coalesce(productName,productid)

If both productName and productid fields have values for a given event, the coalesce function will return the value of the productName field because it appears first in the argument list. The productid field will be ignored by the coalesce function.

Therefore, the value for the productName field will be used to populate the productINFO field if both fields have values for a given event.

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.

The following statements about tags are true: tags are based on field/value pairs and tags categorize events based on a search. Tags are custom labels that can be applied to fields or field values to provide additional context or meaning for your data. Tags can be used to filter or analyze your data based on common concepts or themes. Tags can be created by using various methods, such as search commands, configuration files, user interfaces, etc. Some of the characteristics of tags are:

Tags are based on field/value pairs: This means that tags are associated with a specific field name and a specific field value. For example, you can create a tag called “alert” for the field name “status” and the field value “critical”. This means that only events that have status=critical will have the “alert” tag applied to them.

Tags categorize events based on a search: This means that tags are defined by a search string that matches the events that you want to tag. For example, you can create a tag called “web” for the search string sourcetype=access_combined. This means that only events that match the search string sourcetype=access_combined will have the “web” tag applied to them.

The following statements about tags are false: tags are case-insensitive and tags are designed to make data more understandable. Tags are case-sensitive and tags are designed to make data more searchable. Tags are case-sensitive: This means that tags must match the exact case of the field name and field value that they are associated with. For example, if you create a tag called “alert” for the field name “status” and the field value “critical”, it will not apply to events that have status=CRITICAL or Status=critical. Tags are designed to make data more searchable: This means that tags can help you find relevant events or patterns in your data by using common concepts or themes. For example, if you create a tag called “web” for the search string sourcetype=access_combined, you can use tag=web to find all events related to web activity.

The correct answer is B. Pivot1.

In Splunk, Pivot is a tool that uses data models to generate reports and dashboard panels without the need for users to write or understand Splunk’s Search Processing Language (SPL)1. Data models enable users of Pivot to create compelling reports and dashboards1. When a Pivot user designs a pivot report, they select the data model that represents the category of event data that they want to work with1. Then they select a dataset within that data model that represents the specific dataset on which they want to report1. This makes Pivot a powerful tool for users who need to create visualizations but do not have a deep understanding of SPL1.

Creating a field alias in Splunk does not modify or remove the original field. Instead, the alias allows the same data to be accessed using a different field name without affecting the original field.

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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.

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 correct answer is D. | eval notNULL = “” fillnull value=0 notNULL

Option A is incorrect because it is missing a comma between the “0” and the notNULL in the if function. The correct syntax for the if function is if (condition, true_value, false_value).

Option B is incorrect because it is missing the false_value argument in the if function. The correct syntax for the if function is if (condition, true_value, false_value).

Option C is incorrect because it uses the nullfill command, which only replaces null values, not empty strings. The nullfill command is equivalent to fillnull value=null.

Option D is correct because it uses the eval command to assign an empty string to the notNULL field, and then uses the fillnull command to replace the empty string with a zero. The fillnull command can replace any value with a specified replacement, not just null values.

Workflow actions allow interaction with external resources.

Extract: “GET workflow actions create an HTTP link using field values. Clicking the link performs an HTTP GET request.”

Thus, creating a GET workflow action retrieves weather data using Zip_Code.

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.

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

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 span option is used to specify the time span for the timechart command. The span value can be a number followed by a time unit, such as h for hour, d for day, w for week, etc. The span value determines how the data is grouped into time buckets. For example, span=12h means that the data is grouped into 12-hour intervals. The timespan option is not a valid option for the timechart command2

1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, timechart command.

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.

An event type in Splunk is defined by a search string that returns a specific set of events. The search string index=games sourcetype=score player=* score>9999 is valid because it filters events based on specific criteria directly within the main search command. This search will find all events in the games index with a sourcetype of score, where the player field exists, and the score is greater than 9999. This specificity and direct filtering make it suitable for defining an event type.

The type of workflow action that sends field values to an external resource (e.g. a ticketing system) is POST. A POST workflow action allows you to send a POST request to a URI location with field values or static values as arguments. For example, you can use a POST workflow action to create a ticket in an external system with information from an event.

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

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

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

 The following commands are used when creating visualizations: geom, geostats, and iplocation. Visualizations are graphical representations of data that show trends, patterns, or comparisons. Visualizations can have different types, such as charts, tables, maps, etc. Visualizations can be created by using various commands that transform the data into a suitable format for the visualization type. Some of the commands that are used when creating visualizations are:

geom: This command is used to create choropleth maps that show geographic regions with different colors based on some metric. The geom command takes a KMZ file as an argument that defines the geographic regions and their boundaries. The geom command also takes a field name as an argument that specifies the metric to use for coloring the regions.

geostats: This command is used to create cluster maps that show groups of events with different sizes and colors based on some metric. The geostats command takes a latitude and longitude field as arguments that specify the location of the events. The geostats command also takes a statistical function as an argument that specifies the metric to use for sizing and coloring the clusters.

iplocation: This command is used to create location-based visualizations that show events with different attributes based on their IP addresses. The iplocation command takes an IP address field as an argument and adds some additional fields to the events, such as Country, City, Latitude, Longitude, etc. The iplocation command can be used with other commands such as geom or geostats to create maps based on IP addresses.

Tags are a type of knowledge object that enable you to assign descriptive keywords to events based on the values of their fields. Tags can help you to search more efficiently for groups of event data that share common characteristics, such as functionality, location, priority, etc. For example, you can tag all the IP addresses of your routers as router, and then search for tag=router to find all the events related to your routers. Tags can also help you to normalize data from different sources by using the same tag name for equivalent field values. For example, you can tag the field values error, fail, and critical as severity=high, and then search for severity=high to find all the events with high severity level2

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

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.

A calculated field in Splunk is created using an eval expression, which allows users to perform calculations or transformations on field values during search time.

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.

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

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

Child datasets can be added to root datasets to narrow down the search. Datasets are collections of events that represent your data in a structured and hierarchical way. Datasets can be created by using commands such as datamodel or pivot. Datasets can have different types, such as events, search, transaction, etc. Datasets can also have different levels, such as root or child. Root datasets are base datasets that contain all events from a data model or an index. Child datasets are derived datasets that contain a subset of events from a parent dataset based on some constraints, such as search terms, fields, time range, etc. Child datasets can be added to root datasets to narrow down the search and filter out irrelevant events.

Auto-Extracted fields in Splunk Data Models are derived directly from the indexed data based on the existing fields within the events. These fields are identified and extracted by Splunk automatically, without the need for explicit field extractions configured by the user. Understanding the characteristics of Auto-Extracted fields is crucial for effectively managing Data Models and utilizing them in Pivot tables for analysis.

A. Auto-Extracted fields can be hidden in Pivot. This is true. When building a Data Model, you have the option to hide certain fields from appearing in Pivot, making the Pivot table cleaner and more focused on the fields that are most relevant for analysis. This helps in reducing clutter and focusing on the data that matters most to the users.

B. Auto-Extracted fields can have their data type changed. This statement is not typically accurate for Auto-Extracted fields. The data type of an Auto-Extracted field is determined by Splunk based on the field's content in the indexed data. While you can assign a type to a field when you manually create a field in a data model, the inherent data type of Auto-Extracted fields is not something that is changed within the Data Model itself.

C. Auto-Extracted fields can be given a friendly name for use in Pivot. This is correct. Within Data Models, you can assign a more user-friendly, descriptive name to an Auto-Extracted field. This feature is particularly useful in making Data Models more intuitive and easier to use for those who may not be familiar with the original field names or when the original field names are not descriptive or user-friendly.

D. Auto-Extracted fields can be added if they already exist in the dataset with constraints. This is true. Auto-Extracted fields are based on fields that already exist in the data. When you define a dataset within a Data Model, you can apply constraints to narrow down the events that the dataset includes. The Auto-Extracted fields are then identified from this constrained dataset. This means that the fields must already be present in the data that meets the dataset's constraints to be available for auto-extraction.

In summary, Auto-Extracted fields in Splunk Data Models offer a flexible and efficient way to utilize existing data fields within Pivot tables, with options to rename them for clarity and hide unnecessary fields to streamline data analysis.

The 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.

A comparison operator is a symbol that compares two values and returns a Boolean result (true or false)2. Splunk supports various comparison operators such as <, >, =, !=, <=, >=, IN and LIKE2. However, ?= is not a valid comparison operator in Splunk and will cause a syntax error if used in a search string2. Therefore, option E is correct, while options A, B, C and D are incorrect because they are valid comparison operators in Splunk

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.

To filter for only IP addresses that appear more than five times in the search results for index=games, you can use a combination of the stats and where commands. The stats command counts the occurrences of each IP address and assigns the count to IP_count. The where command then filters the results to include only those IP addresses with a count greater than five.

Here is how the complete search would look:

index=games | stats count as IP_count by IP | where IP_count > 5

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

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

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.

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.

The correct answer is C. _time.

The timechart command creates a time series chart with corresponding table of statistics, 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. In this case, the split-by field is action, which means that the chart will have different lines for different actions, such as accept, reject, or fail2. The count function will calculate the number of events for each action in each time bin1.

For example, the following image shows a timechart of the count by action for a similar search3:

As you can see, the x-axis is populated by the _time field, which represents the time range of the search. The y-axis is populated by the count function, which represents the number of events for each action. The legend shows the different values of the action field, which are used to split the chart into different series.

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 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.

This is because an event type is a knowledge object that assigns a user-defined name to a set of events that match a specific search criteria. For example, you can create an event type named successful_purchase for events that have sourcetype=access_combined, status=200, and action=purchase. Then, you can use eventtype=successful_purchase as a search term to find those events. You can also use event types to create alerts, reports, and dashboards. You can learn more about event types from the Splunk documentation1. The other options are incorrect because they do not describe what an event type is. A log level measurement is a field that indicates the severity of an event, such as info, warn, or error. A knowledge object that is applied before fields are extracted is a source type, which identifies the format and structure of the data. Either a log, a metric, or a trace is a type of data that Splunk can ingest and analyze, but not an event type.

In this case, the outer join is applied, which means that all rows from the outer (left) table will be included, even if there are no matching rows in the inner (right) table. The result will include all five rows from the outer table, with the matched data from the inner table where employeeNumber matches. Rows without matching employeeNumber values will have null values for the fields from the inner table.

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 statement that pivot is used for creating reports and dashboards is true. Pivot is a graphical interface that allows you to create tables, charts, and visualizations from data models. Data models are structured datasets that define how data is organized and categorized. Pivot does not create datasets, but uses existing ones.

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.