SPLK-1002 Splunk Core Certified Power User Exam Question and Answers

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.

To configure a Search Workflow Action to use the same time range as the original search, you need to check the option "Use the same time range as the search that created the field listing." This will ensure the time range is inherited from the original search.

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

The 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.

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( < 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.

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

In this search, count will appear on the y-axis2. This search uses the chart command to create a chart of the count of events over host for events that have status not equal to 2002. The chart command creates a table with one column for each value of the field after the over clause and one row for each value of the field after the by clause (if any)2. The values in the table are calculated by applying the function before the over clause to the events in each group2. In this case, the chart command creates a table with one column for each host and one row for the count of events for each host. The y-axis of the chart shows the values of the count function applied to each host. Therefore, option C is correct, while options A and B are incorrect because they appear on the x-axis or as labels of the chart.

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

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.

Field aliases are used to normalize different field names that contain the same type of data (like IP addresses) across multiple sourcetypes or sources, making the data CIM compliant without re-extracting the fields.

A field alias is a way to assign an alternative name to an existing field without changing the original field name or value2. You can use field aliases to make your field names more consistent or descriptive across different sources or sourcetypes2. When you run a search without any transforming commands in Smart Mode, Splunk automatically identifies and displays interesting fields in your results2. Interesting fields are fields that appear in at least 20 percent of events or have high variability among values2. If you have created a field alias based on an original field, both the original field name and the alias name will appear in the Interesting Fields list if they meet these criteria2. However, only one of them will appear in each event depending on which one you have specified in your search string2. Therefore, option B is correct, while options A, C and D are incorrect.

The correct answer is 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 transaction command groups related events based on shared field values. The maxspan argument specifies the maximum time span allowed between the earliest and latest events in a transaction.

Extract: “Use the maxspan argument to specify the maximum duration of a transaction. Events that exceed this time span are not grouped into the same transaction.”

Therefore, transaction user host maxspan=15s groups events by user and host only when they occur within 15 seconds of each other.

The where command can be used to complete the search below.

… | where failure > successes

The where command is a search command that allows you to filter events based on complex or custom criteria. The where command can use any boolean expression or function to evaluate each event and determine whether to keep it or discard it. The where command can also compare fields or perform calculations on fields using operators such as > , < , =, +, -, etc. The where command can be used after any transforming command that creates a table or a chart.

The search string below does the following:

It uses … to represent any search criteria or commands before the where command.

It uses the where command to filter events based on a comparison between two fields: failure and successes.

It uses the greater than operator ( > ) to compare the values of failure and successes fields for each event.

It only keeps events where failure is greater than successes.

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.

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

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

Thus, the correct answer is Sourcetypes.

The correct answer is A. When a log file has values that are separated by the same character, for example, commas.

The Field Extractor (FX) is a utility in Splunk Web that allows you to create new fields from your events by using either regular expressions or delimiters. The FX provides a graphical interface that guides you through the steps of defining and testing your field extractions1.

The FX supports two field extraction methods: regular expression and delimited. The regular expression method works best with unstructured event data, such as logs or messages, that do not have a consistent format or structure. You select a sample event and highlight one or more fields to extract from that event, and the FX generates a regular expression that matches similar events in your data set and extracts the fields from them1.

The delimited method is designed for structured event data: data from files with headers, where all of the fields in the events are separated by a common delimiter, such as a comma, a tab, or a space. You select a sample event, identify the delimiter, and then rename the fields that the FX finds1.

Therefore, you would select the delimited field extraction method when you have a log file that has values that are separated by the same character, for example, commas. This method will allow you to easily extract the fields based on the delimiter without writing complex regular expressions.

The other options are not correct because they are not suitable for the delimited field extraction method. These options are:

B. When a log file contains empty lines or comments: This option does not indicate that the log file has a structured format or a common delimiter. The delimited method might not work well with this type of data, as it might miss some fields or include some unwanted values.

C. With structured files such as JSON or XML: This option does not require the delimited method, as Splunk can automatically extract fields from JSON or XML files by using indexed extractions or search-time extractions2. The delimited method might not work well with this type of data, as it might not recognize the nested structure or the special characters.

D. When the file has a header that might provide information about its structure or format: This option does not indicate that the file has a common delimiter between the fields. The delimited method might not work well with this type of data, as it might not be able to identify the fields based on the header information.

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

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 in searches, reports, dashboards, and data models like any other extracted field1.

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 named discount that is equal to 90% of the total field value for each event2.

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

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

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

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

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 command to show the total bytes for each unique combination of page and server is index=web | stats sum (bytes) BY page server. In Splunk, the stats command is used to calculate aggregate statistics over the dataset, such as count, sum, avg, etc. When using the BY clause, it groups the results by the specified fields. The correct syntax does not include commas or the word ‘AND’ between the field names. Instead, it simply lists the field names separated by spaces within the BY clause.

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

In Splunk, when using the chart command, the useother parameter can be set to false (f) to remove the 'OTHER' category, which is a bucket that Splunk uses to aggregate low-cardinality groups into a single group to simplify visualization. Here's how the options break down:

A. | chart count over CurrentStanding by Action useother=fThis command correctly sets the useother parameter to false, which would prevent the 'OTHER' category from being displayed in the resulting visualization.

B. | chart count over CurrentStanding by Action usenull=f useother=tThis command has useother set to true (t), which means the 'OTHER' category would still be included, so this is not a correct option.

C. | chart count over CurrentStanding by Action limit=10 useother=fSimilar to option A, this command also sets useother to false, additionally imposing a limit to the top 10 results, which is a way to control the granularity of the chart but also to remove the 'OTHER' category.

D. | chart count over CurrentStanding by Action limit-10This command has a syntax error (limit-10 should be limit=10) and does not include the useother=f clause. Therefore, it would not remove the 'OTHER' category, making it incorrect.

The correct answers to rewrite the syntax to remove the 'OTHER' category are options A and C, which explicitly set useother=f.

The correct answer is C. Distributable streaming. This is because the eval command is a type of command that can run on the indexers before the results are sent to the search head. This reduces the amount of data that needs to be transferred and improves the search performance. Distributable streaming commands can operate on each event or result individually, without depending on other events or results. You can learn more about the types of commands and how they affect search performance from the Splunk documentation1.

The correct answer is B. A field added by an automatic lookup.

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 use any field as a source, as long as the field is extracted before the calculated field is defined1.

An automatic lookup is a way to enrich events with additional fields from an external source, such as a CSV file or a database. An automatic lookup can add fields to events based on the values of existing fields, such as host, source, sourcetype, or any other extracted field2. An automatic lookup is performed before the calculated fields are defined, so the fields added by the lookup can be used as sources for the calculated fields3.

Therefore, a calculated field can use a field added by an automatic lookup as a source.

https://docs.splunk.com/Documentation/Splunk/8.1.1/SearchReference/ConditionalFunctions

The relationship between data models and pivots is that data models provide the datasets for pivots. Data models are collections of datasets that represent your data in a structured and hierarchical way. Data models define how your data is organized into objects and fields. Pivots are user interfaces that allow you to create data visualizations that present different aspects of a data model. Pivots let you select options from menus and forms to create charts, tables, maps, etc., without writing any SPL code. Pivots use datasets from data models as their source of data. Pivots and data models are not the same thing, as pivots are tools for visualizing data models. Pivots do not provide datasets for data models, but rather use them as inputs.

Therefore, only statement A is true about the relationship between data models and pivots.

The 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.

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.

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 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 fillnull command replaces null values in fields with a specified replacement value.

Extract: “Use fillnull to replace null field values with a string or numeric value that you specify.”

Thus, in this case, all null field values are replaced with “Unknown.”

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

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.

The eval command is a Splunk command that allows you to create or modify fields using expressions .

The if function is an expression that evaluates a condition and returns a value based on whether the condition is true or false. The syntax of the if function is if(X,Y,Z), where X is the condition, Y is the value to return if X is true, and Z is the value to return if X is false.

The isnotnull function is an expression that returns true if the argument is not null, and false otherwise. The syntax of the isnotnull function is isnotnull(X), where X is the argument to check.

Therefore, the expression if (isnotnull (src), src, host) returns the value of src if it is not null, and the value of host otherwise. This means that it will provide a new value for host from src if it exists, and keep the original value of host otherwise.

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.

Highlighted search terms indicate matching search results in Splunk, which means that they show which parts of your events match your search string2. For example, if you search for error OR fail, Splunk will highlight error or fail in your events to show which events match your search string2. Therefore, option D is correct, while options A, B and C are incorrect because they are not indicated by highlighted search terms.

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.

The Field Extractor Utility (FX) is a tool that helps you extract fields from your events using a graphical interface or by manually editing the regular expression2. The FX has a button that displays events that do not contain extracted fields, which is the Non-Matches button2. The Non-Matches button shows you the events that do not match the regular expression that you have defined for your field extraction2. This way, you can check if your field extraction is accurate and complete2. Therefore, option B is correct, while options A, C and D are incorrect because they are not buttons that display events that do not contain extracted fields.

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.

The correct answer is D. Search.

A workflow action is a knowledge object that enables a variety of interactions between fields in events and other web resources. Workflow actions can create HTML links, generate HTTP POST requests, or launch secondary searches based on field values1.

There are three types of workflow actions that can be set up using Splunk Web: GET, POST, and Search2.

GET workflow actions create typical HTML links to do things like perform Google searches on specific values or run domain name queries against external WHOIS databases2.

POST workflow actions generate an HTTP POST request to a specified URI. This action type enables you to do things like creating entries in external issue management systems using a set of relevant field values2.

Search workflow actions launch secondary searches that use specific field values from an event, such as a search that looks for the occurrence of specific combinations of ipaddress and http_status field values in your index over a specific time range2.

Therefore, the workflow action type that performs a secondary search is Search.

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.

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

Splunk event types are based on search strings that cannot contain pipes or subsearches.

Extract: “Event type search strings must not contain a pipe (|) and cannot include transforming or filtering commands.”

Thus, only option B is a valid event type definition because it is a simple base search without pipelines or subsearches.

The correct answer is C. When events contain unstructured data.

The regular expression method works best with unstructured event data, such as log files or text messages, where the fields are not separated by a common delimiter, such as a comma or space1. You select a sample event and highlight one or more fields to extract from that event, and the field extractor generates a regular expression that matches similar events in your dataset and extracts the fields from them1. The regular expression method provides several tools for testing and refining the accuracy of the regular expression. It also allows you to manually edit the regular expression1.

The delimiters method is designed for structured event data: data from files with headers, where all of the fields in the events are separated by a common delimiter, such as a comma or space1. You select a sample event, identify the delimiter, and then rename the fields that the field extractor finds1. This method is simpler and faster than the regular expression method, but it may not work well with complex or irregular data formats1.

The appendcols command in Splunk is used to connect an additional table of data directly to the right side of the existing table. It appends the results of a subsearch as new fields to the current results, effectively adding columns to the existing table.

https://docs.splunk.com/Documentation/Splunk/8.1.2/SearchReference/Chart

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

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 correct answer is D. limit=0. This is because the limit argument specifies the maximum number of series to display in the chart. If you set limit=0, no series filtering occurs and all values are returned. You can learn more about the limit argument and how it works with the agg argument from the Splunk documentation1. The other options are incorrect because they are not valid values for the limit argument. The limit argument expects an integer value, not a string or a wildcard. You can learn more about the syntax and usage of the timechart command from the Splunk documentation23.

"Calculated fields can reference all types of field extractions and field aliasing, but they cannot reference lookups, event types, or tags."

Data models and pivot are both knowledge objects in Splunk that allow you to analyze and visualize your data in different ways. Data models are collections of datasets that represent your data in a structured and hierarchical way. Data models define how your data is organized into objects and fields. Pivot is a user interface that allows you to create data visualizations that present different aspects of a data model. Pivot does not require users to input SPL searches on data models, but rather lets them select options from menus and forms. Data models are not created out of datasets called pivots, but rather pivots are created from datasets in data models.

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.

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

Tags are aliases or alternative names for field values in Splunk. They can make your data more understandable by using common or descriptive terms instead of cryptic or technical terms. For example, you can tag a field value such as “200” with “OK” or “success” to indicate that it is a HTTP status code for a successful request. Tags are case sensitive, meaning that “OK” and “ok” are different tags. Tags are created at search time, meaning that they are applied when you run a search on your data. Tags are searched by using the syntax tag:: < tagname > , where  < tagname >  is the name of the tag you want to search for.

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.

Event types in Splunk are saved as static search strings. The example index=server_485 sourcetype=BETA_726 code=917 is a simple search that can be saved as an event type, as it does not contain dynamic processing commands like stats or inputlookup, which are not valid for event types.

Event types are custom categories of events that are based on search criteria. Event types can be used to label events with meaningful names, such as error, success, login, logout, etc. Event types can also be used to create transactions, alerts, reports, dashboards, etc. Event types can be created in two ways:

By going to the Settings menu and clicking Event Types > New. This will open a form where you can enter the name, description, search string, app context, and tags for the event type.

By selecting an event in search results and clicking Event Actions > Build Event Type. This will open a dialog box where you can enter the name and description for the event type. The search string will be automatically populated based on the selected event.

Event types cannot be created by using the searchtypes command in the search bar, as this command does not exist in Splunk. Event types can also be created by editing the event_type stanza in the transforms.conf file, not the props.conf file.

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

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.

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

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.

In Splunk, a field alias is used to create an alternative name for an existing field, making it easier to refer to data in a consistent manner across different searches and reports. Field aliases can be applied to both calculated fields and extracted fields. Calculated fields are those that are created using eval expressions, while extracted fields are typically those parsed from the raw data at index time or search time. This flexibility allows users to streamline their searches by using more intuitive field names without altering the underlying data. Field aliases cannot be applied to data in a lookup table, specific individual fields within a dataset, or directly to a host, source, or sourcetype.

The correct answer is A. Field alias123.

In Splunk, a field alias is a knowledge object that you can use to assign an alternate name to a field3. This can be particularly useful when you want to normalize your data to comply with the Splunk Common Information Model (CIM)12.

The CIM provides a methodology for normalizing values to a common field name1. It acts as a search-time schema to define relationships in the event data while leaving the raw machine data intact2. By using field aliases, you can map vendor fields to common fields that are the same for each data source in a given domain4. This allows you to correlate events from different source types by normalizing these different occurrences to a common structure and naming convention1.