Splunk SPLK-1002 Splunk Core Certified Power User Exam Exam Practice Test

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.

When using the Field Extractor (FX) tool in Splunk and the tool fails to extract a value from all appropriate events, there are specific steps you can take to improve the extraction process. These steps involve interacting with the FX tool and possibly adjusting the extraction method:

A. Select an additional sample event with the Field Extractor (FX) and highlight the missing value in the event. This approach allows Splunk to understand the pattern better by providing more examples. By highlighting the value in another event where it wasn't extracted, you help the FX tool to learn the variability in the data format or structure, improving the accuracy of the field extraction.

D. Edit the regular expression manually. Sometimes the FX tool might not generate the most accurate regular expression for the field extraction, especially when dealing with complex log formats or subtle nuances in the data. In such cases, manually editing the regular expression can significantly improve the extraction process. This involves understanding regular expression syntax and how Splunk extracts fields, allowing for a more tailored approach to field extraction that accounts for variations in the data that the automatic process might miss.

Options B and C are not typically related to improving field extraction within the Field Extractor tool. Re-ingesting data (B) does not directly impact the extraction process, and changing to a delimited extraction method (C) is not always applicable, as it depends on the specific data format and might not resolve the issue of missing values across events.

A report is a way to save a search and its results in a format that you can reuse and share with others2. A report is also a type of knowledge object, which is an entity that you create to add knowledge to your data and make it easier to search and analyze2. Therefore, option A is correct. A report can be scheduled, which means that you can configure it to run at regular intervals and send the results to yourself or others via email or other methods2. Therefore, option B is correct. A report can run a script, which means that you can specify a script file to execute when the report runs and use it to perform custom actions or integrations2. Therefore, option C is correct. Therefore, option D is correct because all of the above statements are true for reports.

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 Field Extractor (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 allows you to create field extractions that persist as knowledge objects, which are entities that you create to add knowledge to your data and make it easier to search and analyze2. Field extractions are methods that extract fields from your raw data using various techniques such as regular expressions, delimiters or key-value pairs2. When you create a field extraction using the FX, you can save it as a knowledge object that applies to your data at search time2. You can also manage and share your field extractions with other users in your organization2. Therefore, option C is correct, while options A, B and D are incorrect because they do not describe the use of the FX.

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 reports1.

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=

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 characteristics2.

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 search3.

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 search3.

By default, acceleration is determined automatically based on the data source in the Splunk Common Information Model (CIM) add-on. The Splunk CIM Add-on is an app that provides common data models for various domains, such as network traffic, web activity, authentication, etc. The CIM Add-on allows you to normalize and enrich your data using predefined fields and tags. The CIM Add-on also allows you to accelerate your data models for faster searches and reports. Acceleration is a feature that pre-computes summary data for your data models and stores them in tsidx files. Acceleration can improve the performance and efficiency of your searches and reports that use data models.

By default, acceleration is determined automatically based on the data source in the CIM Add-on. This means that Splunk will decide whether to enable or disable acceleration for each data model based on some factors, such as data volume, data type, data model complexity, etc. However, you can also manually enable or disable acceleration for each data model by using the Settings menu or by editing the datamodels.conf file.

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

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.

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.

https://docs.splunk.com/Documentation/Splunk/8.0.2/Knowledge/SetupaGETworkflowaction

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.

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::, where  is the name of the tag you want to search for.

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 fillnull command can be used to replace null values in specific fields. The correct equivalent expression for the given eval statement would involve using fillnull twice, once for field1 to replace null values with 0, and once for field2 to replace null values with "NO-VALUE".

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.

This search string is not valid: index=web status=50* | chart count over host,status2. This search string uses an invalid syntax for the chart command. The chart command requires one field after the over clause and optionally one field after the by clause. However, this search string has two fields after the over clause separated by a comma. This will cause a syntax error and prevent the search from running. Therefore, option A is correct, while options B and C are incorrect because they are valid search strings that use the chart command correctly.

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 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 B. Root event dataset. This is because root event datasets are defined by a constraint that filters out events that are not relevant to the dataset. A constraint for a root event dataset is a simple search that returns a fairly wide range of data, such as sourcetype=access_combined. Without a constraint, a root event dataset would include all the events in the index, which is not useful for data modeling. You can learn more about how to design data models and add root event datasets from the Splunk documentation1. The other options are incorrect because root transaction datasets and root search datasets have different ways of defining their datasets, such as transaction definitions or complex searches, and root child datasets are not a valid type of root dataset.

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

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

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

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

The search below would limit an “alert” tag to the “host” field.

tag::host=alert

The search does the following:

It uses tag syntax to filter events by tags. Tags are custom labels that can be applied to fields or field values to provide additional context or meaning for your data.

It specifies tag::host=alert as the tag filter. This means that it will only return events that have an “alert” tag applied to their host field or host field value.

It uses an equal sign (=) to indicate an exact match between the tag and the field or field value.

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

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

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

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

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

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

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

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

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

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

The timeline is a graphical representation of your search results that shows the distribution of events over time2. You can use the timeline to zoom in or out of a specific time range or to select one or more bars on the timeline to filter your results by that time range2. However, these actions will not re-run the search, but rather refine the existing results based on the selected time range2. Therefore, options B, C and D are correct, while option A is incorrect because zooming out will re-run the search with a broader time range.

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.

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.

The fillnull command in Splunk is used to handle missing data within search results. It plays a crucial role in data normalization and preparation, especially before performing statistical analyses or visualizations.

A. Replace empty values with a specified value: This is the correct answer. The fillnull command is specifically designed to replace null values (empty values) with a specified default value. This is particularly useful in ensuring consistency within your data, especially when performing operations that require numerical values or when you want to distinguish between genuinely missing data and zeroes, for instance.

Example Usage: ... | fillnull value=0 This command would replace all null values in the search results with 0.

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 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 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.

https://docs.splunk.com/Documentation/Splunk/8.1.0/SearchReference/Chart

https://docs.splunk.com/Documentation/Splunk/8.1.0/SearchReference/Stats

Tags are a type of knowledge object that enable you to assign descriptive keywords to events. Tags can reference any of the following knowledge objects: extracted fields, field aliases, calculated fields, lookups, and event types. Tags cannot reference other tags or search macros. Tags are applied to events at search time based on the values of the fields that they reference2

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

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.

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.

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.

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.

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.

Field aliases can be applied at the level of event types to rename or alias fields without modifying the raw data. They do not apply to tags, indexes, or sourcetypes directly.

The commands pivot and tstats both leverage data model summaries for faster search performance. The datamodel command itself is used to inspect or operate on data models. The transaction and stats commands do not directly access data model summaries.

https://docs.splunk.com/Documentation/Splunk/8.0.2/Search/Abouttransactions

In other cases, it's usually better to use the stats command, which performs more efficiently, especially in a distributed environment. Often there is a unique ID in the events and stats can be used.

Data model datasets have a hierarchical relationship with each other, meaning they have parent-child relationships. Data models can contain multiple dataset hierarchies. There are three types of dataset hierarchies: event, search, and transaction.

https://docs.splunk.com/Splexicon:Datamodeldataset

The correct answer is B. Use the field values in an HTTP error event to create a new ticket in an external system.

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 example that would use a POST workflow action is B. Use the field values in an HTTP error event to create a new ticket in an external system. This example requires sending an HTTP POST request to the URI of the external system with the field values from the event as arguments.

The other examples would use different types of workflow actions. These examples are:

A. Perform an external IP lookup based on a domain value found in events: This example would use a GET workflow action to create a link to an external IP lookup service with the domain value as a parameter.

C. Launch secondary Splunk searches that use one or more field values from selected events: This example would use a Search workflow action to run another Splunk search with the field values from the event as search terms.

D. Open a web browser to look up an HTTP status code: This example would also use a GET workflow action to create a link to a web page that explains the meaning of the HTTP status code.

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.

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.

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.

In Splunk, calculated fields allow you to create new fields using expressions that can transform or combine the values of existing fields. Although all options provided might seem viable, when selecting only one option that is most representative of a calculated field, we typically refer to:

D. Extracted fields: Calculated fields are often based on fields that have already been extracted from your data. Extracted fields are those that Splunk has identified and pulled out from the event data based on patterns, delimiters, or other methods such as regular expressions or automatic extractions. These fields can then be used in expressions to create calculated fields.

For example, you might have an extracted field for the time in seconds, and you want to create a calculated field for the time in minutes. You would use the extracted field in a calculation to create the new field.

It's important to note that although fields generated within a search string (A) and regular expressions (C) can also be used in the calculation of a new field, and lookup tables (B) can be used to enrich data, option D is typically what one refers to when discussing calculated fields, as it implies a direct transformation or calculation based on fields that have been extracted from the raw data.

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.

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

The correct answer is D. Event types do not include a time range.

The explanation is as follows:

Event types are a categorization system that help you make sense of your data by matching events with the same search string1. Event types are applied to events at search time and can be used as search terms or filters12.

Saved reports are results saved from a search action that can show statistics and visualizations of events3. Saved reports can be run anytime, and they fetch fresh results each time they are run34. Saved reports can be shared with other users and added to dashboards4.

The main difference between event types and saved reports is that event types do not include a time range, while saved reports do14. This means that event types can match events from any time period, while saved reports are limited by the time range specified when they are created or run14.

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.

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.

A report that is scheduled to run every 15 minutes but takes 17 minutes to complete is in danger of being skipped or deferred2. This means that Splunk may skip some scheduled runs of the report if they overlap with previous runs that are still in progress or defer them until the previous runs are finished2. This can affect the accuracy and timeliness of the report results and notifications2. Therefore, option A is correct, while options B, C and D are incorrect because they are not consequences of a report taking longer than its schedule interval.

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

The explanation is as follows:

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

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

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

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

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

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.

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

A macro is a way to reuse a piece of SPL code in different searches. A macro can take arguments, which are variables that can be replaced by different values when the macro is called. A macro can also contain another macro within it, which is called a nested macro1.

To reference a macro in a search, you need to enclose the macro name in backtick characters (). For example, if you have a macro named my_macro` that takes one argument, you can reference it in a search by using the following syntax:

| my_macro(argument) | ...

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

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

And you reference it in a search with:

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

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

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

The correct answer is C. Creates reports without using SPL. This is because the Pivot tool is a feature of Splunk that allows you to report on a specific data set without using the Splunk Search Processing Language (SPL). You can use a drag-and-drop interface to design and generate pivots that present different aspects of your data in the form of tables, charts, and other visualizations. You can learn more about the Pivot tool from the Splunk documentation1 or watch a video tutorial2. The other options are incorrect because they do not describe the features of the Pivot tool. The Pivot tool requires data models and datasets to define the data that you want to work with. Data models and datasets are designed by the knowledge managers in your organization. You can learn more about data models and datasets from the Splunk documentation3. The Pivot tool does not create lookups, which are tables that match field values to other field values. You can create lookups using SPL or the Lookup Editor. You can learn more about lookups from the Splunk documentation.

The building block of a data model. Each data model is composed of one or more data model datasets. Each dataset within a data model defines a subset of the dataset represented by the data model as a whole.

Data model datasets have a hierarchical relationship with each other, meaning they have parent-child relationships. Data models can contain multiple dataset hierarchies. There are three types of dataset hierarchies: event, search, and transaction.

https://docs.splunk.com/Splexicon:Datamodeldataset

The correct answer is C. A macro is a portion of a search that can be reused in multiple places.

A macro is a way to reuse a piece of SPL code in different searches. A macro can be any part of a search, such as an eval statement or a search term, and does not need to be a complete command. A macro can also 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 create a macro, you need to define its name, definition, arguments, and description in the Settings > Advanced Search > Search Macros page in Splunk Web or in the macros.conf file. To use a macro in a search, you need to enclose the macro name in backtick characters (`) and provide values for the arguments if any1.

For example, if you have a macro named my_macro that takes one argument named object and has the following definition:

search sourcetype= object

You can use it in a search by writing:

my_macro(web)

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

search sourcetype=web

The benefits of using macros are that they can simplify complex searches, reduce errors, improve readability, and promote consistency1.

The other options are not correct because they describe other types of knowledge objects in Splunk, not macros. These objects are:

A. An event type is a method of categorizing 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 reports2.

B. A field alias is a way to associate an additional (new) name with an existing field name. A field alias can be used to normalize fields from different sources that have different names but represent the same data. Field aliases can also be used to rename fields for clarity or convenience3.

D. An alert is a knowledge object that enables you to schedule searches for specific events and trigger actions when certain conditions are met. An alert can be used to monitor your data for anomalies, errors, or other patterns of interest and notify you or others when they occur4.

This search uses the transaction command to group events that share a common value for JSESSIONID into transactions1. The transaction command assigns a duration field to each transaction, which is the difference between the latest and earliest timestamps of the events in the transaction1. The search then uses the timechart command to create a time-series chart of the average duration of each transaction1. Therefore, option A is correct because it describes the search accurately. Option B is incorrect because the search does not use the stats command or the pause field. Option C is incorrect because the transaction command does not require the startswith and endswith options, although they can be used to specify how to identify the beginning and end of a transaction1. Option D is incorrect because the transaction command does not have to be the last command in the search pipeline, although it is often used near the end of a search1.

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 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, 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 transaction command is a Splunk command that finds transactions based on events that meet various constraints1.

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 member1.

The transaction command adds some fields to the raw events that are part of the transaction12. These fields are:

duration: The difference, in seconds, between the timestamps for the first and last events in the transaction12.

eventcount: The number of events in the transaction12.

closed_txn: A Boolean field that indicates whether the transaction is closed or evicted2. A transaction is closed if it meets one of the following conditions: maxevents, maxpause, maxspan, or startswith2. A transaction is evicted if it does not meet any of these conditions and exceeds the memory limit specified by maxopentxn or maxopenevents23.

Therefore, evicted transactions can be distinguished from non-evicted transactions by checking the value of the closed_txn field. The closed_txn field is set to 0, or false, for evicted transactions and 1, or true for non-evicted, or closed, transactions23.

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.

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.