SPLK-1002 Questions and Answers

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 macro is a way to save a commonly used search string as a variable that you can reuse in other searches1. When you create a macro, you can define arguments that are placeholders for values that you specify at execution time1. The argument values are used to resolve the search string when the macro is invoked, not when it is created1. Therefore, statements B and C are true, while statements A and D are false.

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

After manually editing a regular expression (regex) that was created using the Field Extractor (FX) UI, it is no longer possible to edit the field extraction in the FX UI. The FX UI is a tool thathelps you extract fields from your data using delimiters or regular expressions. The FX UI can generate a regex for you based on your selection of sample values or you can enter your own regex in the FX UI. However, if you edit the regex manually in the props.conf file, the FX UI will not be able to recognize the changes and will not let you edit the field extraction in the FX UI anymore. You will have to use the props.conf file to make any further changes to the field extraction. Changes made manually cannot be reverted in the FX UI, as the FX UI does not keep track of the changes made in the props.conf file. It is possible to manually edit a regex that was created using the FX UI, as long as you do it in the props.conf file.

Therefore, only statement B is true about manually editing a regex.

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.

The eval command is used to create new fields or modify existing fields based on an expression2. The sort command is used to sort the results by one or more fields in ascending or descending order2. If you want to convert numeric field values to strings and also sort on those values, you should use the sort command first, then use the eval command to convert the values to strings2. This way, the sort command will use the original numeric values for sorting, rather than the converted string values which may not sort correctly. Therefore, option C is correct, while options A, B and D are incorrect.

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 data model command allows you to run searches on data models that have been accelerated1. The syntax for using the data model command is | datamodel [search ]1. Therefore, option A is the correct way to use the data model command to search fields in the data model within the web dataset. Options B and C are incorrect because they do not follow the syntax for the data model command. Option D is incorrect because it does not use the data model command at all.

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 eval command supports a number of functions that you can use in your expressions to perform calculations, conversions, string manipulations and more2. One of the eval command functions is tostring(), which converts a numeric value to a string value2. Therefore, option D is correct, while options A, B and C are incorrect because they are not valid eval command functions.

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.

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 command sourcetype=access_combined | transaction JSESSIONID does three things:

It filters the events by the sourcetype access_combined, which is a predefined sourcetype for Apache web server logs.

It groups the events by the field JSESSIONID, which is a unique identifier for each user session.

It creates a single event from each group of events that share the same JSESSIONID value. This single event will have some additional fields created by the transaction command, such as duration, eventcount, and startime.

Therefore, the statements B, C, and D are true.

Data model acceleration is a feature that speeds up searches on data models by creating and storing summaries of the data model datasets1. To enable data model acceleration, you must have administrative permissions or the accelerate_datamodel capability1. Therefore, option D is correct. Accelerated data models cannot be edited unless you disable the acceleration first1. Therefore, option B is correct. Private data models cannot be accelerated because they are not visible to other users1. Therefore, option C is correct. Root events can be accelerated as long as they are not based on a search string1. Therefore, option A is incorrect.

In Splunk, the order of operations for Boolean logic in the where command follows this sequence:

Parentheses: Operations inside parentheses are evaluated first.

NOT: The NOT operator is evaluated after parentheses.

AND: The AND operator is evaluated next.

OR: Finally, the OR operator is evaluated last.

This order ensures that expressions within parentheses are given priority, followed by negations (NOT), conjunctions (AND), and finally disjunctions (OR).

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.

When performing an outer join in Splunk using the | join employeeNumber type=outer command, it combines the rows from both tables based on the employeeNumber field. An outer join returns all rows from both tables, with matching rowsfrom both sides where available. If there is no match, the result is NULL on the side of the join where there is no match.

In the provided tables, there are five rows in the first table and three in the second. Since it’s an outer join, all rows from both tables will be returned. This means the new table will have a total of eight rows, combining the matched rows and the unmatched rows from both tables.

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.

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.

 There are several ways to access the field extractor. The option that automatically identifies data type, source type, and sample event is Fields sidebar > Extract New Field. The field extractor is a tool that helps you extract fields from your data using delimiters or regular expressions. The field extractor can generate a regex for you based on your selection of sample values or you can enter your own regex in the field extractor. The field extractor can be accessed by using various methods, such as:

Fields sidebar > Extract New Field: This is the easiest way to access the field extractor. The fields sidebar is a panel that shows all available fields for your data and their values. When you click on Extract New Field in the fields sidebar, Splunk will automatically identify the data type, source type, and sample event for your data based on your current search criteria. You can then use the field extractor to select sample values and generate a regex for your new field.

Event Actions > Extract Fields: This is another way to access the field extractor. Event actions are actions that you can perform on individual events in your search results, such as viewing event details, adding to report, adding to dashboard, etc. When you click on Extract Fields in the event actions menu, Splunk will use the current event as the sample event for your data and askyou to select the source type and data type for your data. You can then use the field extractor to select sample values and generate a regex for your new field.

Settings > Field Extractions > New Field Extraction: This is a more advanced way to access the field extractor. Settings is a menu that allows you to configure various aspects of Splunk, such as indexes, inputs, outputs, users, roles, apps, etc. When you click on New Field Extraction in the Settings menu, Splunk will ask you to enter all the details for your new field extraction manually, such as app context, name, source type, data type, sample event, regex, etc. You can then use the field extractor to verify or modify your regex for your new field.

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.

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.

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.

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.

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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.

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 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.

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

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.

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, 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.

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

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

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

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

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

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

The correct answer is C. Product, count: addtocart, count: remove, count: purchase, sum: addtocart, sum: remove, sum: purchase1.

In Splunk, the chart command is used to create a table or a chart visualization from your data2. The chart command takes at least one function and one field, and optionally another field to group by2.

In the given search, the chart command is used with two functions (count and sum), two fields (domain and price), and two fields to group by (product and action). The usenull=f and useother=f options are used to exclude null values and other values from the chart2.

The chart command creates a table with headers that match the order of the fields and functions in the command1. The headers for the count function are prefixed with count:, and the headers for the sum function are prefixed with sum:1. The values of the product and action fields are used as the suffixes for the headers1.

Therefore, the table headers created by this command are Product, count: addtocart, count: remove, count: purchase, sum: addtocart, sum: remove, and sum: purchase1.

The time range specified for a historical search defines the amount of data fetched from the index matching that time range2. A historical search is a search that runs over a fixed period of time in the past2. When you run a historical search, Splunk searches the index for events that match your search string and fall within the specified time range2. Therefore, option B is correct, while options A and C are incorrect because they are not what the time range defines for a historical search.

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 recommended approach when using the Splunk Common Information Model (CIM) add-on to normalize data is A. Consult the CIM data model reference tables. This is because the CIM data model reference tables provide detailed information about the fields and tags that are expected for each dataset in a data model. By consulting the reference tables, you can determine which data models are relevant for your data source and how to map your data fields to the CIM fields. You can also use the reference tables to validate your data and troubleshoot any issues with normalization. You can find the CIM data model reference tables in the Splunk documentation1 or in the Data Model Editor page in Splunk Web2. The other options are incorrect because they are not related to the CIM add-on or data normalization. The authentication command is a custom command that validates events against the Authentication data model, but it does not help you to normalize other types of data. The correlation command is a search command that performs statistical analysis on event fields, but it does not help you to map your data fields to the CIM fields. The CIM event type reference tables do not exist, as event types are not part of the CIM add-on.

The correct answer is C. _time.

The timechart command creates a time series chart with corresponding table of statistics, with time used as the X-axis1. You can specify a split-by field, where each distinct value of the split-by field becomes a series in the chart1. In this case, the split-by field is action, which means that the chart will have different lines for different actions, such as accept, reject, or fail2. The count function will calculate the number of events for each action in each time bin1.

For example, the following image shows a timechart of the count by action for a similar search3:

As you can see, the x-axis is populated by the _time field, which represents the time range of the search. The y-axis is populated by the count function, which represents the number of events for each action. The legend shows the different values of the action field, which are used to split the chart into different series.

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

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

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

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

In Splunk, 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.

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

When using the Field Extractor (FX) in Splunk for regex field extraction, it's important to select the context in which you want to perform the extraction. The context is essentially the subset of data you're focusing on for your field extraction task.

D. Sourcetype or source: This is the correct option. In the initial steps of using the Field Extractor tool, you're prompted to choose a data type for your field extraction. The options available are typically based on the nature of your data and how it's organized in Splunk. "Sourcetype" refers to the kind of data you're dealing with, a categorization that helps Splunk apply specific processing rules. "Source" refers to the origin of the data, like a specific log file or data input. By selecting either a sourcetype or source, you're narrowing down the dataset on which you'll perform the regex extraction, making it more manageable and relevant.

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.

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 span option is used to specify the time span for the timechart command. The span value can be a number followed by a time unit, such as h for hour, d for day, w for week, etc. The span value determines how the data is grouped into time buckets. For example, span=12h means that the data is grouped into 12-hour intervals. The timespan option is not a valid option for the timechart command2

1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, timechart command.

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`.

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 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.

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.

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.

The Splunk Common Information Model (CIM) add-on helps you normalize your data from different sources and make it easier to analyze and report on it3. One of the functionalities that the CIM add-on relies on to normalize fields with different names is field aliases3. Field aliases allow you to assign an alternative name to an existing field without changing the original field name or value2. By using field aliases, you can map different field names from different sources or sourcetypes to a common field name that conforms to the CIM standard3. Therefore, option B is correct, while options A, C and D are incorrect.

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.

The Field Extractor (FX) allows you to use regular expressions (regex) to extract fields from your events using a graphical interface or by manually editing the regex2. When you use the FX to perform a regex field extraction, you can use the require option to specify a string that must be present in an event for it to be included in the extraction2. This way, you can filter out events that do not contain the required string and focus on the events that are relevant for your extraction2. Therefore, option D is correct, while options A, B and C are incorrect.

As mentioned before, there are two types of workflow actions: GET and POST1. Both types of workflow actions can be executed from search results by clicking on an event field value that has a workflow action configured for it1. Another type of workflow action is Search, which runs another search based on the field value1. Therefore, options A, B and D are correct, while option C is incorrect because LOOKUP is not a type of workflow action.

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 timechart command is used to create a time-series chart of statistical values based on your search results2. You can use the timechart command with a by clause to split the results by one or more fields and create multiple series in the chart2. However, you can only list one field after the by clause when using the timechart command because _time is already implied as the x-axis of the chart2. Therefore, option B is correct, while options A, C and D are incorrect.

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 forthose 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.