Reference:
A calculated field is a field that you create based on the value of another field or fields1. You can use calculated fields to enrich your data with additional information or to transform your data into a more useful format1. Calculated fields 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 pairs1. Therefore, option B is correct, while options A, C and D are incorrect because tags, output fields for a lookup, and fields generated from a search string are not types of extracted fields.

Explanation
As mentioned before, a calculated field is a field that you create based on the value of another field or fields2. A calculated field can be based on extracted fields, which are fields that are extracted from your raw data using various methods such as regular expressions, delimiters or key-value pairs2. Therefore, option B is correct, while options A, C and D are incorrect because they are not types of fields that a calculated field can be based on.

The maxspan function of the transaction command allows you to set the maximum total time between the
earliest and latest events returned. The maxspan function is an argument that can be used with the transaction
command to specify the start and end constraints for the transactions. The maxspan function takes a time
modifier as its value, such as 30s, 5m, 1h, etc. The maxspan function sets the maximum time span between the
first and last events in a transaction. If the time span between the first and last events exceeds the maxspan
value, the transaction will be split into multiple transactions.

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.
References:
* Splunk Documentation on the join command.
* Splunk Community discussions on the usage of join and types of joins.

Explanation/Reference: https://docs.splunk.com/Documentation/CIM/4.18.0/User/Overview

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 namedmy_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
References:
Use search macros in searches

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.

Explanation
Explanation/Reference: https://docs.splunk.com/Documentation/Splunk/8.1.1/Data/Extractfieldsfromfileswithstructureddata

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
References:
* Use search macros in searches

Explanation/Reference: https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/ TagandaliasfieldvaluesinSplunkWeb

The correct answer is A. chart, timechart, stats, eventstats.
Transforming commands are commands that change the format of the search results into a table or a
chart.They can be used to perform statistical calculations, create visualizations, or manipulate data in various
ways1.
Transactions are groups of events that share some common values and are related in some way.Transactions
can be defined by using the transaction command or by creating a transaction type in the transactiontypes.conf
file2.
Some transforming commands can be used with transactions to create tables or charts based on the transaction
fields. These commands include:
chart: This command creates a table or a chart that shows the relationship between two or more fields.It
can be used to aggregate values, count occurrences, or calculate statistics3.
timechart: This command creates a table or a chart that shows how a field changes over time.It can be
used to plot trends, patterns, or outliers4.
stats: This command calculates summary statistics on the fields in the search results, such as count, sum,
average, etc.It can be used to group and aggregate data by one or more fields5.
eventstats: This command calculates summary statistics on the fields in the search results, similar to
stats, but it also adds the results to each event as new fields. It can be used to compare events with the
overall statistics.
These commands can be applied to transactions by using the transaction fields as arguments. For example, if
you have a transaction type named “login” that groups events based on the user field and has fields such as
duration and eventcount, you can use the following commands with transactions:
| chart count by user: This command creates a table or a chart that shows how many transactions each
user has.
| timechart span=1h avg(duration) by user: This command creates a table or a chart that shows the
average duration of transactions for each user per hour.
| stats sum(eventcount) as total_events by user: This command creates a table that shows the total
number of events for each user across all transactions.
| eventstats avg(duration) as avg_duration: This command adds a new field named avg_duration to each
transaction that shows the average duration of all transactions.
The other options are not valid because they include commands that are not transforming commands or cannot
be used with transactions. These commands are:
diff: This command compares two search results and shows the differences between them. It is not a
transforming command and it does not work with transactions.
datamodel: This command retrieves data from a data model, which is a way to organize and categorize
data in Splunk. It is not a transforming command and it does not work with transactions.
pivot: This command creates a pivot report, which is a way to analyze data from a data model using a
graphical interface. It is not a transforming command and it does not work with transactions.
References:
About transforming commands
About transactions
chart command overview
timechart command overview
stats command overview
[eventstats command overview]
[diff command overview]
[datamodel command overview]
[pivot command overview]

The correct answer is A. chart, timechart, stats, eventstats.
Transforming commands are commands that change the format of the search results into a table or a chart.
They can be used to perform statistical calculations, create visualizations, or manipulate data in various ways1.
Transactions are groups of events that share some common values and are related in some way. Transactions can be defined by using the transaction command or by creating a transaction type in the transactiontypes.conf file2.
Some transforming commands can be used with transactions to create tables or charts based on the transaction fields. These commands include:
* chart: This command creates a table or a chart that shows the relationship between two or more fields. It can be used to aggregate values, count occurrences, or calculate statistics3.
* timechart: This command creates a table or a chart that shows how a field changes over time. It can be used to plot trends, patterns, or outliers4.
* stats: This command calculates summary statistics on the fields in the search results, such as count, sum, average, etc. It can be used to group and aggregate data by one or more fields5.
* eventstats: This command calculates summary statistics on the fields in the search results, similar to stats, but it also adds the results to each event as new fields. It can be used to compare events with the overall statistics.
These commands can be applied to transactions by using the transaction fields as arguments. For example, if you have a transaction type named “login” that groups events based on the user field and has fields such as duration and eventcount, you can use the following commands with transactions:
* | chart count by user : This command creates a table or a chart that shows how many transactions each user has.
* | timechart span=1h avg(duration) by user : This command creates a table or a chart that shows the average duration of transactions for each user per hour.
* | stats sum(eventcount) as total_events by user : This command creates a table that shows the total number of events for each user across all transactions.
* | eventstats avg(duration) as avg_duration : This command adds a new field named avg_duration to each transaction that shows the average duration of all transactions.
The other options are not valid because they include commands that are not transforming commands or cannot be used with transactions. These commands are:
* diff: This command compares two search results and shows the differences between them. It is not a transforming command and it does not work with transactions.
* datamodel: This command retrieves data from a data model, which is a way to organize and categorize data in Splunk. It is not a transforming command and it does not work with transactions.
* pivot: This command creates a pivot report, which is a way to analyze data from a data model using a graphical interface. It is not a transforming command and it does not work with transactions.
References:
* About transforming commands
* About transactions
* chart command overview
* timechart command overview
* stats command overview
* [eventstats command overview]
* [diff command overview]
* [datamodel command overview]
* [pivot command overview]

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.

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.

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.

Reference: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 marks1.
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 expression1. 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 transaction command groups events that share a common value in a specified field, such as JSESSIONID, and that occur within a specified time range. The search command filters the results to show only the events that match the given value of JSESSIONID. This search groups the events by JSESSIONID and then shows only the events that have the value SD462K101C2F267 for JSESSIONID2
1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, transaction command.

The Splunk Common Information Model (CIM) is a collection of data models that apply a common structure
and naming convention to data from any source. A data model is a type of knowledge object that defines the
structure and relationships of fields in a dataset. A data model can have one or more datasets, which are
subsets of the data model that represent different aspects of the data. For example, the Network Traffic data
model has datasets such as All Traffic, DNS, HTTP, etc. The CIM contains 28 pre-configured data models that
cover various domains such as authentication, network traffic, web, email, etc. The CIM is implemented as an
add-on that contains the JSON files for the data models, documentation, and tools that support the consistent,
normalized treatment of data for maximum efficiency at search time23
1: Splunk Core Certified Power User Track, page 10. 2: Splunk Documentation, Overview of the Splunk
Common Information Model 1. 3: Splunkbase, Splunk Common Information Model (CIM) 2.

Explanation
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.