Splunk SPLK-1002 Online Practice Questions and Exam Preparation

Splunk SPLK-1002 Online Questions & Answers

• Question 261:

  Which command is used to create choropleth maps?

  A. geostats
  B. cluster
  C. geom
  C. geom

• Question 262:

  __________ datasets can be added to root dataset to narrow down the search

  A. parent
  B. extracted
  C. event
  D. child
  D. child

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  Explanation

  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.

• Question 263:

  There are several ways to access the field extractor.

  Which option automatically identifies data type, source type, and sample event?

  A. Event Actions > Extract Fields
  B. Fields sidebar > Extract New Field
  C. Settings > Field Extractions > New Field Extraction
  D. Settings > Field Extractions > Open Field Extraction
  B. Fields sidebar > Extract New Field

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  Explanation

  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 ask you 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.

• Question 264:

  A data model can consist of what three types of datasets?

  A. Pivot, searches, and events.
  B. Pivot, events, and transactions.
  C. Searches, transactions, and pivot.
  D. Events, searches, and transactions.
  D. Events, searches, and transactions.

• Question 265:

  Which of the following statements is true about the root dataset of a data model?

  A. It can contain transforming commands as long as it is a root search dataset.
  B. It will automatically contain knowledge objects associated with the base search.
  C. It must contain the transaction command if it is a root transaction dataset.
  D. It can only contain a base search with no transforming commands.
  B. It will automatically contain knowledge objects associated with the base search.

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  Explanation

  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.

• Question 266:

  Which type of workflow action sends field values to an external resource (e.g.

  a ticketing system)?

  A. POST
  B. Search
  C. GET
  D. Format
  A. POST

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  Explanation

  The type of workflow action that sends field values to an external resource (e.g. a ticketing system) is POST.

  A POST workflow action allows you to send a POST request to a URI location with field values or static values as arguments. For example, you can use a POST workflow action to create a ticket in an external system with information from an event.

• Question 267:

  When would transaction be used instead of stats?

  A. To group events based on a single field value.
  B. To see results of a calculation.
  C. To have a faster and more efficient search.
  D. To group events based on start/end values.
  D. To group events based on start/end values.

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  Explanation

  The transaction command is used to group events that are related by some common fields or conditions, such as start/end values, time span, or pauses. The stats command is used to calculate statistics on a group of events by a common field value.

  References

  Splunk Community

  Splunk Transaction - Exact Details You Need

• Question 268:

  Two separate results tables are being combined using the |join command. The outer table has the following values:

  Refer to following Tables

  

  The line of SPL used to join the tables is: | join employeeNumber type=outer

  How many rows are returned in the new table?

  A. Zero
  B. Five
  C. Eight
  D. Three
  C. Eight

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  Explanation

  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 rows from 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.

  References:

  Splunk Documentation on the join command.

  Splunk Community discussions on the usage of join and types of joins.

• Question 269:

  Which of the following transforming commands can be used with transactions?

  A. chart, timechart, stats, eventstats
  B. chart, timechart, stats, diff
  C. chart, timeehart, datamodel, pivot
  D. chart, timecha:t, stats, pivot
  A. chart, timechart, stats, eventstats

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  Explanation

  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 ways. 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 file. 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 statistics. 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 outliers. 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 fields. 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.

  References:

  About transforming commands

  About transactions

  chart command overview

  timechart command overview

  stats command overview

  [eventstats command overview]

  [diff command overview]

  [datamodel command overview]

  [pivot command overview]

• Question 270:

  Which search string would only return results for an event type called success ful_purchases?

  A. tag=success ful_purchases
  B. Event Type:: successful purchases
  C. successful_purchases
  D. event type--success ful_purchases
  C. successful_purchases

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  Explanation

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

  event type--), or have a typo (success ful_purchases). You can learn more about how to use event types in searches from the Splunk documentation.