SPLK-1002 Sample Questions Answers

The correct answer is A. | where 10yearAnnerversary=Renewal-MonthYear.

The where command is used to filter the search results based on an expression that evaluates to true or false. The where command can compare two fields, two values, or a field and a value. The where command can also use functions, operators, and wildcards to create complex expressions1.

The syntax for the where command is:

| where

The expression can be a comparison, a calculation, a logical operation, or a combination of these. The expression must evaluate to true or false for each event.

To compare two fields with the where command, you need to use the field names without any quotation marks. For example, if you want to find events where the values for the 10yearAnnerversary field match the values for the Renewal-MonthYear field, you can use the following syntax:

| where 10yearAnnerversary=Renewal-MonthYear

This will return only the events where the two fields have the same value.

The other options are not correct because they use quotation marks around the field names, which will cause the where command to interpret them as string values instead of field names. For example, if you use:

| where ‘10yearAnnerversary’=‘Renewal-MonthYear’

This will return no events because there are no events where the string value ‘10yearAnnerversary’ is equal to the string value ‘Renewal-MonthYear’.

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

https://docs.splunk.com/Documentation/Splunk/8.1.0/SearchReference/ConversionFunctions#tostring.28X.2CY.29

The tostring function in the eval command converts a numeric value to a string value. It can take an optional second argument that specifies the format of the string value. Some of the possible formats are:

hex: converts the numeric value to a hexadecimal string.

commas: adds commas to separate thousands in the numeric value.

duration: converts the numeric value to a human-readable duration string, such as “2h 3m 4s”.

Therefore, the formats A, B, and D can be used with the tostring function.

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.

This is because event types are added to events as a field named eventtype, and you can use this field as a search term to find events that match a specific event type. For example, eventtype=successful_purchases returns all events that have been categorized as successful purchases by the event type definition. The other options are incorrect because they either use a different field name (tag), a different syntax (Event Type:: or event type—), or have a typo (success ful_purchases). You can learn more about how to use event types in searches from the Splunk documentation1.

A chart is a graphical representation of your search results that shows the relationship between two or more fields2. You can display a chart in stack mode by changing the Stack Mode option in the Format menu2. Stack mode allows you to stack multiple series on top of each other in a chart to show the cumulative values of each series2. Therefore, option C is correct, while options A, B and D are incorrect because they are not ways to display a chart in stack mode.

Stacked charts represent a series in a single bar with multiple sections. A chart is a graphical representation of data that shows trends, patterns, or comparisons. A chart can have different types, such as column, bar, line, area, pie, etc. A chart can also have different modes, such as split-series, multi-series, stacked, etc. A stacked chart is a type of chart that shows multiple series in a single bar or area with different sections for each series

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

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.

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.

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.

Arguments are defined within the macro search string by using dollar signs on either side of the argument name, such as arg1 or fragment.

References

Search macro examples

Define search macros in Settings

Use search macros in searches

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 Splunk Common Information Model (CIM) Add-on includes a variety of data models designed to normalize data from different sources to allow for cross-source reporting and analysis. Among the data models included, Alerts (Option B) and Email (Option D) are part of the CIM. The Alerts data model is used for data related to alerts and incidents, while the Email data model is used for data pertaining to email messages and transactions. User permissions (Option A) and Databases (Option C) are not data models included in the CIM; rather, they pertain to aspects of data access control and specific types of data sources, respectively, which are outside the scope of the CIM's predefined data models.

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

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

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

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.

During the validation step of the Field Extractor workflow, you can remove values that aren’t a match for the field you want to define2. The validation step allows you to review and edit the values that have been extracted by the FX and make sure they are correct and consistent2. You can remove values that aren’t a match by clicking on them and selecting Remove Value from the menu2. This will exclude them from your field extraction and update the regular expression accordingly2. Therefore, option A is correct, while options B and C are incorrect because they are not actions that you can perform during the validation step of the Field Extractor workflow.

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

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

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

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

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

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.

A space is an implied AND in a search string, which means that it acts as a logical operator that returns events that match both terms on either side of the space2. For example, status=200 method=GET will return events that have both status=200 and method=GET2. Therefore, option B is correct, while options A, C and D are incorrect because they are not implied by a space in a search string.

The fillnull command replaces null values with 0 by default, if the value argument is not specified. You can use the value argument to specify a different value to replace null values with, such as N/A or NULL.

A delimiter field extraction is a method of extracting fields from data that uses a character or a string to separate fields in each event. A delimiter field extraction can be performed by using the Field Extractor (FX) tool or by editing the props.conf file. A delimiter field extraction can be applied to any file format that uses a delimiter to separate fields, such as CSV, TSV, PSV, etc. A CSV file is a comma-separated values file that uses commas as delimiters. Therefore, a CSV file can be extracted using a delimiter field extraction.

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

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

To create a macro that accepts arguments, you must include the number of arguments in parentheses at the end of the macro name1. For example, my_macro(3) is a macro that accepts three arguments. The number of arguments in the macro name must match the number of arguments in the definition1. Therefore, option A is correct, while options B, C and D are incorrect.

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 following search would create a graph similar to the one below:

index_internal sourcetype=Savesplunker | fields sourcetype, status | transaction status maxspan=1d | timechart count by status

The search does the following:

It uses index_internal to specify the internal index that contains Splunk logs and metrics.

It uses sourcetype=Savesplunker to filter events by the sourcetype that indicates the Splunk Enterprise Security app.

It uses fields sourcetype, status to keep only the sourcetype and status fields in the events.

It uses transaction status maxspan=1d to group events into transactions based on the status field with a maximum time span of one day between the first and last events in a transaction.

It uses timechart count by status to create a time-based chart that shows the count of transactions for each status value over time.

The graph shows the following:

It is a line graph with two lines, one yellow and one blue.

The x-axis is labeled with dates from Wed, Apr 4, 2018 to Tue, Apr 10, 2018.

The y-axis is labeled with numbers from 0 to 15.

The yellow line represents “shipped” and the blue line represents “success”.

The yellow line has a steady increase from 0 to 15, while the blue line has a sharp increase from 0 to 5, then a decrease to 0, and then a sharp increase to 10.

The graph is titled “Type”.

Therefore, option C is the correct answer.

A calculated field in Splunk is designed to automatically add fields at search time using an eval expression. This feature allows users to define new fields based on existing data without needing to manually include an eval command in every search. Calculated fields simplify repeated search tasks by embedding the eval logic directly into the field configuration.

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.

POST workflow actions are custom actions that send a POST request to a web server when you click on a field value in your search results. POST workflow actions can be configured with various options, such as label name, base URL, URI parameters, post arguments, app context, etc. One of the options that are required to create a POST workflow action is post arguments. Post arguments are key-value pairs that are sent in the body of the POST request to provide additional information to the web server. Post arguments can include field values from your data by using dollar signs around the field names.

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

Therefore, option B is the correct answer.

To use a search macro in a search string, you need to place a back tick character (`) before and after the macro name1. You also need to use the same number of arguments as defined in the macro2. The macro weekly sales (2) has two arguments: Price and AmountSold. Therefore, you need to provide two values for these arguments when you call the macro.

The option A is incorrect because it uses parentheses instead of back ticks around the macro name. The option B is incorrect because it uses underscores instead of spaces in the macro name. The option D is incorrect because it uses spaces instead of commas to separate the argument values.

The Splunk Common Information Model (CIM) is an app that contains a set of predefined data models that apply a common structure and naming convention to data from any source. The CIM enables you to use data from different sources in a consistent and coherent way. The CIM contains 28 pre-configured datasets that cover various domains such as authentication, network traffic, web, email, etc. The data models included in the CIM are configured with data model acceleration turned on by default, which means that they are optimized for faster searches and analysis. Data model acceleration creates and maintains summary data for the data models, which reduces the amount of raw data that needs to be scanned when you run a search using a data model.

Splunk Core Certified Power User Track, page 10. : Splunk Documentation, About the Splunk Common Information Model.

The 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.

In Splunk, you can create an event type from the search window by running a search that would make a good event type, then clicking Save As and selecting Event Type1. This opens the Save as Event Type dialog, where you can provide the event type name and optionally apply tags to it1.

You can also create an event type by editing the eventtypes.conf file and adding a new stanza1. Each stanza in the eventtypes.conf file represents an event type1. The stanza name is the name of the event type, and the search attribute specifies the search string that defines the event type1.

It’s important to note that while you can use the eventtype command in a search to find events associated with a specific event type, adding | eventtype to the SPL and executing the search does not create a new event type1. Similarly, clicking Event Actions > Build Event Type in an event’s detail dropdown does not create a new event type1.

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.

Data model attributes are fields that are added to a dataset from search time field extractions, calculated fields, lookups, or aliases. They can be created within the data model editor or inherited from a parent dataset. They can be edited or removed unless they are required by the data model. They can be added to any type of dataset, not just root search datasets.ReferencesSee About data models, [Define data model attributes], and [Edit data model datasets] in the Splunk Documentation.

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

The correct answer is C. Events where values of field A are equal to values of field B.

The where command is used to filter the search results based on an expression that evaluates to true or false. The where command can compare two fields, two values, or a field and a value. The where command can also use functions, operators, and wildcards to create complex expressions1.

The syntax for the where command is:

| where

The expression can be a comparison, a calculation, a logical operation, or a combination of these. The expression must evaluate to true or false for each event.

To compare two fields with the where command, you need to use the field names without any quotation marks. For example, if you want to find events where the values for the field A match the values for the field B, you can use the following syntax:

| where A=B

This will return only the events where the two fields have the same value.

The other options are not correct because they use different syntax or fields that are not related to the where command. These options are:

A. Events that contain the string value where A=B: This option uses the string value where A=B as a search term, which is not valid syntax for the where command. This option will return events that have the literal text “where A=B” in them.

B. Events that contain the string value A=B: This option uses the string value A=B as a search term, which is not valid syntax for the where command. This option will return events that have the literal text “A=B” in them.

D. Events where field A contains the string value B: This option uses quotation marks around the value B, which is not valid syntax for comparing fields with the where command. Quotation marks are used to enclose phrases or exact matches in a search2. This option will return events where the field A contains the string value “B”.

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.

The search modes determine how Splunk processes your search and displays your results2. There are three search modes: Fast, Smart and Verbose2. The search mode that automatically returns all extracted fields in the fields sidebar is Verbose2. The Verbose mode shows all the fields that are extracted from your events, including default fields, indexed fields and search-time extracted fields2. The fields sidebar is a panel that shows the fields that are present in your search results2. Therefore, option C is correct, while options A and B are incorrect because they are not search modes that automatically return all extracted fields in the fields sidebar.

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

In Splunk, a macro can accept arguments, and the correct syntax for a macro that takes two arguments is macro_name(argument1, argument2). In this case, the macro is called samplemacro, and it accepts two arguments, so the correct format would be samplemacro(1,2). This syntax allows for passing dynamic values into the macro, which can then be used to modify the search dynamically based on the arguments provided.

The 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.

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.

A POST workflow action in Splunk is designed to send data to an external web service by using HTTP POST requests. This type of workflow action can pass a combination of clear text strings and variables derived from the search results or event data. The clear text strings might include static text or predefined values, while the variables are dynamic elements that represent specific fields or values extracted from the Splunk events. This flexibility allows for constructing detailed and context-specific requests to external systems, enabling various integration and automation scenarios. The POST request can include both types of data, making it versatile for different use cases.

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.

Using the export function, you can export search results as XML or JSON2. The export function allows you to save your search results in a structured format that can be used by other applications or tools2. You can use the output_mode parameter to specify whether you want to export your results as XML or JSON2. Therefore, options A and B are correct, while options C and D are incorrect because they are not formats that you can export your search results as.

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

Workflow actions allow interaction with external resources.

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

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

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.

It provides users with a standardized set of field names and tags to normalize data.The Splunk CIM add-on provides a standardized set of field names and data models, which allows users to normalize and categorize data from various sources into a common format. This helps with data interoperability and enables faster, more consistent reporting and searching across different data sources.References:Splunk Documentation - Common Information Model (CIM)

The eval command ‘if’ function requires the following three arguments (in order): boolean expression, result if true, result if false. The eval command is a search command that allows you to create new fields or modify existing fields by performing calculations or transformations on them. The eval command can use various functions to perform different operations on fields. The ‘if’ function is one of the functions that can be used with the eval command to perform conditional evaluations on fields. The ‘if’ function takes three arguments: a boolean expression that evaluates to true or false, a result that will be returned if the boolean expression is true, and a result that will be returned if the boolean expression is false. The ‘if’ function returns one of the two results based on the evaluation of the boolean expression.

Macros in Splunk are defined with the number of arguments inside parentheses after the macro name. For two arguments, the syntax is samplemacro(2). This defines a macro accepting two arguments.

Event types in Splunk are saved searches that categorize data, making it easier to search for specific patterns or criteria within your data. When saving an event type, the search must essentially filter events based on criteria without performing operations that transform or aggregate the data. Here's a breakdown of the options:

A. The search index-server_472 sourcetype-BETA_494 code-488 | stats count by code performs an aggregation operation (stats count by code), which makes it unsuitable for saving as an event type. Event types are meant to categorize data without aggregating or transforming it.

B. The search index=server_472 sourcetype=BETA_494 code=488 [ | inputlookup append=t servercode.csv] includes a subsearch and input lookup, which is typically used to enrich or filter events based on external data. This complexity goes beyond simple event categorization.

C. The search index=server_472 sourcetype=BETA_494 code=488 | stats where code > 200 includes a filtering condition within a transforming command (stats), which again, is not suitable for defining an event type due to the transformation of data.

D. The search index=server_472 sourcetype=BETA_494 code-488 is the correct answer as it purely filters events based on index, sourcetype, and a code field condition without transforming or aggregating the data. This is what makes it suitable for saving as an event type, as it categorizes data based on specific criteria without altering the event structure or content.

In Splunk, the transaction command is used to group events that share common characteristics into a single transaction1. By default, the transaction command groups all matching events into a single transaction1.

However, you can use the maxspan option to limit the time span of the transactions1. If the time span between the first and last event in a transaction exceeds the maxspan value, the transaction command will start a new transaction1.

Therefore, if the maxspan option is not included in the search, the transaction command might produce multiple transactions instead of one if the time span between the first and last event in a transaction exceeds the default maxspan value1.

Here is an example of how you can use the maxspan option in a search:

index=main sourcetype=access_combined | transaction someuniqefield maxspan=1h

In this search, the transaction command groups events that share the same someuniqefield value into a single transaction, but only if the time span between the first and last event in the transaction does not exceed 1 hour1. If the time span exceeds 1 hour, the transaction command will start a new transaction1.

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.

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.

The Splunk Common Information Model (CIM) provides a methodology to normalize data from different sources and source types. The CIM defines a common set of fields and tags for different types of data, such as web, network, email, etc. This allows you to search and analyze data from different sources in a consistent way.

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

Field aliases in Splunk are alternate names assigned to fields. These can be particularly useful for normalizing data from different sources or simply for making field names more intuitive. Once an alias is created for a field, it can be used across various Splunk knowledge objects, enhancing their flexibility and utility.

A. Calculated fields, lookups, event types, and tags: This is the correct answer. Field aliases can indeed be referenced in calculated fields, lookups, event types, and tags within Splunk. When you create an alias for a field, that alias can then be used in these knowledge objects just like any standard field name.

Calculated fields: These are expressions that can create new field values based on existing data. You can use an alias in a calculated field expression to refer to the original field.

Lookups: These are used to enrich your event data by referencing external data sources. If you've created an alias for a field that matches a field in your lookup table, you can use that alias in your lookup configurations.

Event types: These are classifications for events that meet certain search criteria. You can use field aliases in the search criteria for defining an event type.

Tags: These allow you to assign meaningful labels to data, making it easier to search and report on. You can use field aliases in the search criteria that you tag.

The correct answer is B. Transforming.

The explanation is as follows:

The timechart command is a Splunk command that creates a time series chart with corresponding table of statistics12.

A timechart is a statistical aggregation applied to a field to produce a chart, with time used as the X-axis1. You can specify a split-by field, where each distinct value of the split-by field becomes a series in the chart1.

Transforming commands are commands that change the format of the search results into a data structure that can be easily visualized3. Transforming commands often use stats functions to aggregate and summarize data3.

Therefore, the timechart command is an example of a transforming command, as it transforms the search results into a chart and a table using stats functions123.

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

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 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 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