Free SPLK-1002 Exam Dumps Preview

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.

Event types allow users to assign labels to events based on predefined search strings. This helps categorize data and makes it easier to reference specific sets of events in future searches.

References:
Splunk Docs - Event types

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

The search string below creates a table of the total count of mysterymeat corndogs split by user.

| stats count by user | where corndog=mysterymeat

The search string does the following:

It uses the stats command to calculate the count of events for each value of the user field. The stats command creates a table with two columns: user and count.

It uses the where command to filter the results by the value of the corndog field. The where command only keeps the rows where corndog equals mysterymeat.

Therefore, the search string creates a table of the total count of mysterymeat corndogs split by user.

The 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-length. 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 convenience.
The syntax for the coalesce function is: coalesce(<field1>,<field2>,...)
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.
References:
Search Command> Coalesce
USAGE OF SPLUNK EVAL FUNCTION : COALESCE

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.
References:
Splunk Docs: Create event types
Splunk Answers: Defining event types

The Common Information Model (CIM) add-on in Splunk applies preconfigured data models to data at search time. This means that when a search is executed, the CIM add-on uses its predefined data models to normalize and map the relevant data to a common format. This approach ensures that data is interpreted and analyzed consistently across various datasets without modifying the data at index time.
References:
Splunk Docs: About the Common Information Model Splunk Answers: CIM Add-on Data Models

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

References:
Splunk Docs: stats command
Splunk Docs: where command Splunk Answers: Filtering results using stats and where commands

The correct answer is B. Calculated fields can be chained together to create more complex fields.

Calculated fields are fields that are added to events at search time by using eval expressions. They can be used to perform calculations with the values of two or more fields already present in those events.
Calculated fields can be defined with Splunk Web or in the props.conf file.They can be used insearches, reports, dashboards, and data models like any other extracted field. Calculated fields can also be chained together to create more complex fields. This means that you can use a calculated field as an input for another calculated field. For example, if you have a calculated field named total that sums up the values of two fields named price and tax, you can use the total field to create another calculated field named discount that applies a percentage discount to the total field. To do this, you need to define the discount field with an eval expression that references the total field, such as: discount = total * 0.9
This will create a new field nameddiscountthat is equal to 90% of the total field value for each event.
References:
About calculated fields
Chaining calculated fields

An existing accelerated data model can be edited, but the data model must be de-accelerated before any structural edits can be made (Option C). This is because the acceleration process involves pre-computing and storing data, and changes to the data model's structure could invalidate or conflict with the pre-computed data. Once the data model is de-accelerated and edits are completed, it can be re-accelerated to optimize performance.

The correct answer is A. Field alias.
In Splunk, a field alias is a knowledge object that you can use to assign an alternate name to a field.This can be particularly useful when you want to normalize your data to comply with the Splunk Common Information Model (CIM). The CIM provides a methodology for normalizing values to a common field name.It acts as a search-time schema to define relationships in the event data while leaving the raw machine data intact.By using field aliases, you can map vendor fields to common fields that are the same for each data source in a given domain.This allows you to correlate events from different source types by normalizing these different occurrences to a common structure and naming convention.

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.

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.

References:
Splunk Power User Study Guide, Workflow Actions
Splunk Docs: Workflow Actions
"GET workflow actions are used to retrieve external data using URL parameters from event fields."

References:
https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/Setupasearch
workflowaction A workflow action is a link that appears when you click an event field value in your search results. A workflow action can open a web page or run another search based on the field value. There are two types of workflow actions: GET and POST. A GET workflow action appends the field value to the end of a URI and opens it in a web browser. A POST workflow action sends the field value as part of an HTTP request to a web server. When creating a Search workflow action, which is a type of GET workflow action that runs another search based on the field value, the only required field is the search string. The search string defines the search that will be run when the workflow action is clicked. Therefore, option A is correct, while options B, C and D are incorrect because they are not required fields for creating a Search workflow action.

The correct answer is B. eventtype=web_errors.
An event type is a way to categorize events based on a search. An event type assigns a label to events that match a specific search criteria. Event types can be used to filter and group events, create alerts, or generate reports. To search for events that have a specific event type, you need to use the eventtype field with the name of the event type as the value. The syntax for this is:
eventtype=<event_type_name>
For example, if you want to search for events that have the event type web_errors, you can use the following syntax: eventtype=web_errors
This will return only the events that match the search criteria defined by the web_errors event type.
The other options are not correct because they use different syntax or fields that are not related to event types. These options are:
A. tag=web_errors: This option uses the tag field, which is a way to add descriptive keywords to events
based on field values. Tags are different from event types, although they can be used together.Tags can be used to filter and group events by common characteristics.
C. eventtype "web errors": This option uses quotation marks around the event type name, which is not valid
syntax for the eventtype field.Quotation marks are used to enclose phrases or exact matches in a search. D. eventtype (web_errors): This option uses parentheses around the event type name, which is also not valid syntax for the eventtype field.Parentheses are used to group expressions or terms in a search.
References:
About event types
About tags
Search command cheatsheet

In Splunk, a variable for a macro is defined by placing the variable name inside dollar signs, like this: $variable name$. This syntax allows the macro to dynamically replace the variable with the appropriate value when the macro is invoked within a search. Using this method ensures that the search strings can be dynamically adjusted based on the variable's value at runtime.
References:
Splunk Docs: Use macros
Splunk Answers: Defining and Using Macros

As mentioned before, a calculated field is a field that you create based on the value of another field or fields.
A calculated field can be based on extracted fields, which are fields that are extracted from your raw data using various methods such as regular expressions, delimiters or key-value pairs. Therefore, option B is correct, while options A, C and D are incorrect because they are not types of fields that a calculated field can be based on.

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

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

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.
References:
Splunk Docs: Eval command
Splunk Answers: Multiple expressions in eval

The transaction command is used to group events into transactions based on some common characteristics, such as fields, time, or both. The transaction command can also specify start and end constraints for the transactions, such as a field value that indicates the beginning or the end of a transaction. The stats command is used to calculate summary statistics on the events, such as count, sum, average, etc. The stats command cannot group events based on start and end constraints, but only on fields or time buckets.
Therefore, the transaction command should be used instead of the stats command when you need to group events based on start and end constraints.

When using a field value variable with a Workflow Action, the exclamation mark (!) will escape the data. A Workflow Action is a custom action that performs a task when you click on a field value in your search results. A Workflow Action can be configured with various options, such as label name, base URL, URI parameters, post arguments, app context, etc. A field value variable is a placeholder for the field value that will be used to replace the variable in the URL or post argument of the Workflow Action. A field value variable is written as fieldname, where field_name is the name of the field whose value will be used. However, if the field value contains special characters that need to be escaped, such as spaces, commas, etc., you can use the exclamation mark (!) before and after the field value variable to escape the data. For example, if you have a field value variable host, you can write it as !$host! to escape any special characters in the host field value.
Therefore, option B is the correct answer.

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 searches.
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 command. You can also specify whether the macro field takes any arguments and define validation expressions for them.
Search macros can help you make your SPL searches shorter and easier to understand.
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`.

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 documentation. 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.

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.

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.
References:
Splunk Docs: About subsearches
Splunk Docs: Event type creation
Splunk Answers: Using subsearches in event types

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

The 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.

A macro is a way to save a segment of a search string as a variable and reuse it in other searches. A macro can be followed by a pipe, which is a symbol that separates commands in a search pipeline. A pipe may always follow a macro, regardless of who owns the macro, where the macro is defined or how the macro is shared. 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 product. This search will use the macro to filter the events and then calculate the total price for each product. 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 fields sidebar is a panel that shows the fields that are present in your search results. The fields sidebar has two sections: selected fields and interesting fields. 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 fieldscommand. Interesting fields are fields that appear in at least 20 percent of events or have high variability among values. For each field in the fields sidebar, you can select one of the following options:events with this field, rare values, top values by time or top values. If you select rare values, Splunk will automatically pipe your search results to the rare command, which shows the least common values of a field. Therefore, option B is correct, while options A, C and D are incorrect because they do not pipe your search results to the rare command.

References:
https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/FXSelectMeth
odstep The Field Extractor (FX) is a tool that helps you extract fields from your data using delimiters or regular expressions. Delimiters are characters or strings that separate fields in your data. The FX can detect some common delimiters automatically, such as pipes (|), spaces ( ), commas (,), semicolons (;), etc.The FX cannot detect tabs (\t) as delimiters automatically, but you can specify them manually in the FX interface.

https://docs.splunk.com/Documentation/DashApp/0.9.0/DashApp/chartsBub

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.

References:
https://answers.splunk.com/answers/574643/field-showing-an-additional-and-not-visible-value-1.html
To use a macro in a search, you must enclose the macro name and any arguments in single quotation marks. For example, 'my_macro(arg1,arg2)' is a valid way to use a macro with two arguments. You can use macros anywhere in your search string where you would normally use a search command or expression. Therefore, options A and C are valid searches that use macros, while options B and D are invalid because they do not enclose the macros in single quotation marks.

The eval command is used to create new fields or modify existing fields based on an expression. The sort command is used to sort the results by one or more fields in ascending or descending order. 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 strings. 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.

https://docs.splunk.com/Documentation/Splunk/latest/SearchReference/CommonEvalFunct
ions
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.

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-axis.You can specify a split-by field, where each distinct value of the split-by field becomes a series in the chart.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 fail.The count function will calculate the number of events for each action in each time bin.
For example, the following image shows a timechart of the count by action for a similar search: 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.
References:
1:timechart - Splunk Documentation
2:Timechart Command In Splunk With Example - Mindmajix
3:timechart command examples - Splunk Documentation

References:
https://docs.splunk.com/Documentation/PCI/4.1.0/Install/PrivilegedUserActivity
A tag is a descriptive label that you can apply to one or more fields or field values in your events. You can use tags to simplify your searches by replacing long or complex field names or values with short and simple tags. To search for events that contain a tag name, you can use the tag keyword followed by an equal sign and the tag name. You can also use wildcards (*) to match partial tag names. Therefore, option B is correct because it will return events that contain a tag name that starts with Pri. Options A and D are incorrect because they will only return events that contain an exact tag name match. Option C is incorrect because it will return events that contain a tag name that starts with Priv, not Privileged.

The time range specified for a historical search defines the amount of data fetched from the index matching that time range. A historical search is a search that runs over a fixed period of time in the past. When you run a historical search, Splunk searches the index for events that match your searchstring and fall within the specified time range. 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.