Splunk Splunk Certifications SPLK-1002 Questions & Answers

• Question 91:

  How can an existing accelerated data model be edited?

  A. An accelerated data model can be edited once its .tsidx file has expired.

  B. An accelerated data model can be edited from the Pivot tool.

  C. The data model must be de-accelerated before edits can be made to its structure.

  D. It cannot be edited. A new data model would need to be created.

  Correct Answer: C

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

• Question 92:

  When is a GET workflow action needed?

  A. To send field values to an external resource.

  B. To retrieve information from an external resource.

  C. To use field values to perform a secondary search.

  D. To define how events flow from forwarders to indexes.

  Correct Answer: B

• Question 93:

  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

  Correct Answer: C

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

• Question 94:

  When would transaction be used instead of stats?

  A. To see results of a calculation.

  B. To group events based on start/end values.

  C. To have a faster and more efficient search.

  D. To group events based on a single field value.

  Correct Answer: B

• Question 95:

  A macro has another macro nested within it, and this inner macro requires an argument. How can the user pass this argument into the SPL?

  A. An argument can be passed through the outer macro.

  B. An argument can be passed to the outer macro by nesting parentheses.

  C. There is no way to pass an argument to the inner macro.

  D. An argument can be passed to the inner macro by nesting parentheses.

  Correct Answer: D

  The correct answer is D. An argument can be passed to the inner macro by nesting parentheses.

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

  another search macro within it, which is called a nested macro. A nested macro can also take arguments, which can be passed from the outer macro or directly from the search string. To pass an argument to the inner macro, you need to use

  parentheses to enclose the argument value and separate it from the outer macro argument. For example, if you have a search macro named outer_macro (1) that contains another search macro named inner_macro (2), and both macros take

  one argument each, you can pass an argument to the inner macro by using the following syntax:

  outer_macro (argument1, inner_macro (argument2))

  This will replace the argument1 and argument2 with the values you provide in the search string. For example, if you want to pass "foo" as the argument1 and "bar" as the argument2, you can write:

  outer_macro ("foo", inner_macro ("bar"))

  This will expand the macros with the corresponding arguments and run the SPL code contained in them.

  References:

  Search macro examples

  Use search macros in searches

• Question 96:

  When using a field value variable with a Workflow Action, which punctuation mark will escape the data

  A. *

  B. !

  C. ^

  D. #

  Correct Answer: B

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

• Question 97:

  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

  Correct Answer: C

  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 98:

  For choropleth maps,splunk ships with the following KMZ files (select all that apply)

  A. States of the United States

  B. States and provinces of the united states and Canada

  C. Countries of the European Union

  D. Countries of the World

  Correct Answer: AD

  Splunk ships with the following KMZ files for choropleth maps: States of the United States and Countries of the World. A KMZ file is a compressed file that contains a KML file and other resources. A KML file is an XML file that defines

  geographic features and their properties. A KMZ file can be used to create choropleth maps in Splunk by using the geom command. A choropleth map is a type of map that shows geographic regions with different colors based on some

  metric. Splunk ships with two KMZ files that define the geographic regions for choropleth maps:

  States of the United States: This KMZ file defines the 50 states of the United States and their boundaries. The name of this KMZ file is us_states.kmz and it is located in the $SPLUNK_HOME/etc/apps/maps/appserver/static/geo directory.

  Countries of the World: This KMZ file defines the countries of the world and their boundaries. The name of this KMZ file is world_countries.kmz and it is located in the $SPLUNK_HOME/etc/apps/maps/appserver/static/geo directory. Splunk

  does not ship with KMZ files for States and provinces of the United States and Canada or Countries of the European Union. However, you can create your own KMZ files or download them from external sources and use them in Splunk.

• Question 99:

  Which of the following searches will return all clientip addresses that start with 108?

  A. ... | where like (clientip, "108.% )

  B. ... | where (clientip, "108. %")

  C. ... | where (clientip=108. % )

  D. ... | search clientip=108

  Correct Answer: A

• Question 100:

  When would a user select delimited field extractions using the Field Extractor (FX)?

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

  B. When a log file contains empty lines or comments.

  C. With structured files such as JSON or XML.

  D. When the file has a header that might provide information about its structure or format.

  Correct Answer: A

  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.

  References:

  Build field extractions with the field extractor

  Configure indexed field extraction