SPLK-1002 Practice Exam Questions with Answers Splunk Core Certified Power User Exam Certification

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.

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

When using the transaction command in Splunk, the default maximum span between events is set to unlimited. This is indicated by the default value of maxspan=-1, which corresponds to an “all time” time range.

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

 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.

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.

Search workflow actions are custom actions that run a search when you click on a field value in your search results. Search workflow actions can be configured with various options, such as label name, search string, time range, app context, etc. One of the options is to define the time range of the search when creating the workflow action. You can choose from predefined time ranges, such as Last 24 hours, Last 7 days, etc., or specify a custom time range using relative or absolute time modifiers. Search workflow actions do not run as real-time searches by default, but rather use the same time range as the original search unless specified otherwise. Search workflow actions cannot be configured as scheduled searches, as they are only triggered by userinteraction. Search workflow actions can be configured with any valid search string that includes any search command, such as transaction.

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.

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.

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

The 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.

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.

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 is a Splunk app that contains data models to help you normalize data from different sources and formats. The CIM add-on defines a common and consistent way of naming and categorizing fields and events in Splunk. This makes it easier to correlate and analyze data across different domains, such as network, security, web, etc. The CIM add-on does not use machine learning to normalize data, but rather relies on predefined field names and values. The CIM add-on does not contain dashboards that show how to map data, but rather provides documentation and examples on how to use the data models. The CIM add-on is not automatically installed in a Splunk environment, but rather needs to be downloaded and installed from Splunkbase.

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 transaction command is used to group events into transactions based on some common characteristics, such as fields, time, or both. The transaction command can also specify start and end constraints for the transactions, such as a field value that indicates the beginning or the end of a transaction. The stats command is used to calculate summary statistics on the events, such as count, sum, average, etc. The stats command cannot group events based on start and end constraints, but only on fields or time buckets. Therefore, the transaction command should be used instead of the stats command when you need to group events based on start and end constraints.

The search string below creates a table of the total count of mysterymeat corndogs split by user.

| stats count by user | where corndog=mysterymeat

The search string does the following:

It uses the stats command to calculate the count of events for each value of the user field. The stats command creates a table with two columns: user and count.

It uses the where command to filter the results by the value of the corndog field. The where command only keeps the rows where corndog equals mysterymeat.

Therefore, the search string creates a table of the total count of mysterymeat corndogs split by user.

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.

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.

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.

Field aliases are alternative names for fields in Splunk. Field aliases can be used to normalize data across different sources and sourcetypes that have different field names for the same concept. For example, you can create a field alias for src_ip that maps to clientip, source_address, or any other field name that represents the source IP address in different sourcetypes. Field aliases can also be used in lookup file definitions to map fields in your data to fields in the lookup file. For example, you can use a field alias for src_ip to map it to ip_address in a lookup file that contains geolocation information for IP addresses. Field alias names do not replace the original field name, but rather create a copy of the field with a different name. Field alias names are case sensitive when used as part of a search, meaning that src_ip and SRC_IP are different fields.

In Splunk, Pivot is a tool that allows you to report on a specific data set without using the Splunk Search Processing Language (SPL™)1. 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 visualizations12.

One of the features of Pivot is that it allows you to save your reports1. This can be useful when you want to reuse a report or share it with others1. Therefore, it’s not true that users cannot share visualizations created with Pivot or that they must use SPL to find events in a Pivot12. It’s also not true that users cannot create visualizations with Pivot, as creating visualizations is one of the main functions of Pivot12.

The search below groups events by two or more fields (clientip and host), creates transactions with start and end constraints (maxspan=30s and maxpause=5s), and calculates the duration of each transaction.

index=main | transaction clientip host maxspan=30s maxpause=5s

The search does the following:

It filters the events by the index main, which is a default index in Splunk that contains all data that is not sent to other indexes.

It uses the transaction command to group events into transactions based on two fields: clientip and host. The transaction command creates new events from groups of events that share the same clientip and host values.

It specifies the start and end constraints for the transactions using the maxspan and maxpause arguments. The maxspan argument sets the maximum time span between the first and last events in a transaction. The maxpause argument sets the maximum time span between any two consecutive events in a transaction. In this case, the maxspan is 30 seconds and the maxpause is 5 seconds, meaning that any transaction that has a longer time span or pause will be split into multiple transactions.

It creates some additional fields for each transaction, such as duration, eventcount, startime, etc. The duration field shows the time span between the first and last events in a transaction.

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.

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

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

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

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

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

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

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

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

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

 The transaction and stats commands are two ways to group events from one or more data sources based on common fields or time ranges. The transaction command creates a single eventout of a group of related events, while the stats command calculates summary statistics over a group of events. The eval and coalesce commands are used to create or combine fields, not to group events. The format command is used to format the results of a subsearch, not to group events. The top and rare commands are used to rank the most or least common values of a field, not to group events23

1: Splunk Core Certified Power User Track, page 9. 2: Splunk Documentation, transaction command. 3: Splunk Documentation, stats command.

The correct answer is A. chart, timechart, stats, eventstats.

Transforming commands are commands that change the format of the search results into a table or a chart. They can be used to perform statistical calculations, create visualizations, or manipulate data in various ways1.

Transactions are groups of events that share some common values and are related in some way. Transactions can be defined by using the transaction command or by creating a transaction type in the transactiontypes.conf file2.

Some transforming commands can be used with transactions to create tables or charts based on the transaction fields. These commands include:

chart: This command creates a table or a chart that shows the relationship between two or more fields. It can be used to aggregate values, count occurrences, or calculate statistics3.

timechart: This command creates a table or a chart that shows how a field changes over time. It can be used to plot trends, patterns, or outliers4.

stats: This command calculates summary statistics on the fields in the search results, such as count, sum, average, etc. It can be used to group and aggregate data by one or more fields5.

eventstats: This command calculates summary statistics on the fields in the search results, similar to stats, but it also adds the results to each event as new fields. It can be used to compare events with the overall statistics.

These commands can be applied to transactions by using the transaction fields as arguments. For example, if you have a transaction type named “login” that groups events based on the user field and has fields such as duration and eventcount, you can use the following commands with transactions:

| chart count by user : This command creates a table or a chart that shows how many transactions each user has.

| timechart span=1h avg(duration) by user : This command creates a table or a chart that shows the average duration of transactions for each user per hour.

| stats sum(eventcount) as total_events by user : This command creates a table that shows the total number of events for each user across all transactions.

| eventstats avg(duration) as avg_duration : This command adds a new field named avg_duration to each transaction that shows the average duration of all transactions.

The other options are not valid because they include commands that are not transforming commands or cannot be used with transactions. These commands are:

diff: This command compares two search results and shows the differences between them. It is not a transforming command and it does not work with transactions.

datamodel: This command retrieves data from a data model, which is a way to organize and categorize data in Splunk. It is not a transforming command and it does not work with transactions.

pivot: This command creates a pivot report, which is a way to analyze data from a data model using a graphical interface. It is not a transforming command and it does not work with transactions.

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.

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 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 CIMcontains 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 timechart command buckets data in time intervals depending on the selected time range2. The timechart command is similar to the chart command but it automatically groups events into time buckets based on the _time field2. The size of the time buckets depends on the time range that you select for your search. For example, if you select Last 24 hours as your time range, Splunk will use 30-minute buckets for your timechart. If you select Last 7 days as your time range, Splunk will use 4-hour buckets for your timechart2. Therefore, option B is correct, while options A and C are incorrect because they are not factors that affect the size of the time buckets.

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

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

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

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

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

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

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

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

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

In Splunk, a data model's root dataset is the foundational element upon which the rest of the data model is built. The root dataset can be of various types, including search, transaction, or event-based datasets. One of the key features of the root dataset is that it automatically inherits the knowledge objects associated with its base search. These knowledge objects include field extractions, lookups, aliases, and calculated fields that are defined for the base search, ensuring that the root dataset has all necessary contextual information from the outset. This allows users to build upon this dataset with additional child datasets and objects without having to redefine the base search’s knowledge objects.

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`.

An event type is a classification of events based on a search query, which allows for a static set of search criteria. In this case, option A (index=server_48 sourcetype=BETA_881 code=220) represents a simple search without transforming commands (e.g., stats, inputlookup). Event types cannot include transforming commands such as stats or lookup.References:Splunk Documentation - Event Types

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

Transactions are made up of the raw text (the _raw field) of each member, the time and date fields of the earliest member, as well as the union of all other fields of each member1.

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

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

eventcount: The number of events in the transaction12.

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

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

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 extractedfields2. 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.

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

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

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

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

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

Creating a field alias in Splunk does not modify or remove the original field. Instead, the alias allows the same data to be accessed using a different field name without affecting the original field.

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.

To group events by JSESSIONID, the correct search is index=web sourcetype=access_combined | transaction JSESSIONID | search SD470K92802F117 (Option B). The transaction command groups events that share the same JSESSIONID value, allowing for the analysis of all events associated with a specific session as a single transaction. The subsequent search for SD470K92802F117 filters these grouped transactions to include only those related to the specified session ID.

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.

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

A macro is a way to reuse a piece of SPL code in different searches. A macro can be any part of a search, such as an eval statement or a search term, and does not need to be a complete command. A macro can also take arguments, which are variables that can be replaced by different values when the macro is called. A macro can also contain another macro within it, which is called a nested macro1.

To create a macro, you need to define its name, definition, arguments, and description in the Settings > Advanced Search > Search Macros page in Splunk Web or in the macros.conf file. To use a macro in a search, you need to enclose the macro name in backtick characters (`) and provide values for the arguments if any1.

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

search sourcetype= object

You can use it in a search by writing:

my_macro(web)

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

search sourcetype=web

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

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

A. An event type is a method of categorizing events based on a search. An event type assigns a label to events that match a specific search criteria. Event types can be used to filter and group events, create alerts, or generate reports2.

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

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

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.

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.

The Field Extractor (FX) is a tool that helps you extract fields from your events using a graphical interface2. You can create a report using a custom field extracted by the FX and share it with other users in your organization2. However, if another user runs the shared report and no results are returned, there could be two possible reasons. One reason is that the extraction is private, which means that only you can see and use the extracted field2. To make the extraction available to other users, you need to make it global or app-level2. Therefore, option C is correct. Another reason is that the other user does not have access to the index where the events are stored2. To fix this issue, you need to grant the appropriate permissions to the other user for the index2. Therefore, option D is correct. Options A and B are incorrect because they are not related to the field extraction or the report.