Splunk SPLK-1002 Online Practice Questions and Exam Preparation

Splunk SPLK-1002 Online Questions & Answers

• Question 161:

  Which group of users would most likely use pivots?

  A. Users
  B. Architects
  C. Administrators
  D. Knowledge Managers
  A. Users

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  Explanation/Reference:

  Reference:https://docs.splunk.com/Documentation/Splunk/8.0.3/Pivot/IntroductiontoPivot

  A pivot is a tool that allows you to create reports and dashboards using data models without writing any SPL commands. You can use pivots to explore, filter, split and visualize your data using a graphical interface. Pivots are designed for users who want to analyze and report on their data without having to learn the SPL syntax or the underlying structure of the data. Therefore, option A is correct, while options B, C and D are incorrect because they are not the typical group of users who would use pivots.

• Question 162:

  When using the timechart command, how can a user group the events into buckets based on time?

  A. Using the span argument.
  B. Using the duration argument.
  C. Using the interval argument.
  D. Adjusting the fieldformat options.
  A. Using the span argument.

• Question 163:

  Which of the following statements about tags is true?

  A. Tags are case insensitive.
  B. Tags can make your data more understandable.
  C. Tags are created at index time.
  D. Tags are searched by using the syntax tag :: .
  B. Tags can make your data more understandable.

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  Explanation/Reference:

  Tags are a knowledge object that allow you to assign an alias to one or more field values . Tags are applied to events at search time and can be used as search terms or filters . Tags can help you make your data more understandable by replacing cryptic or complex field values with meaningful names . For example, you can tag the value 200 in the status field as success, or tag the value 404 as not_found .

• Question 164:

  Which of the following are required to create a POST workflow action?

  A. Label, URI, search string.
  B. XMI attributes, URI, name.
  C. Label, URI, post arguments.
  D. URI, search string, time range picker.
  C. Label, URI, post arguments.

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  Explanation/Reference:

  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.

• Question 165:

  A user wants to create a workflow action that will retrieve a specific field value from an event and run a search in a new browser window

  in the user's Splunk instance. What kind of workflow action should they create?

  A. A Run workflow action, because the user is running a new search with a specific field value from an event returned in the user's search.
  B. A Search workflow action, because the user is running a new search with a specific field value from an event returned in the user's search.
  C. A POST workflow action, because the search is being sent to the user's current Splunk instance.
  D. A GET workflow action, because a field value needs to be retrieved from the events returned in the user's search.
  B. A Search workflow action, because the user is running a new search with a specific field value from an event returned in the user's search.

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  Explanation/Reference:

  A Search workflow action is the appropriate choice when a user wants to retrieve a specific field value from an event and run a search in a new browser window within their Splunk instance (Option B). This type of workflow action allows users to define a search that utilizes field values from selected events as parameters, enabling more detailed investigation or context-specific analysis based on the original search results.

• Question 166:

  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/Reference:

  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 167:

  What other syntax will produce exactly the same results as | chart count over vendor_action by user?

  A. | chart count by vendor_action, user
  B. | chart count over vendor_action, user
  C. | chart count by vendor_action over user
  D. | chart count over user by vendor_action
  A. | chart count by vendor_action, user

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  Explanation/Reference:

  https://docs.splunk.com/Documentation/Splunk/8.1.2/SearchReference/Chart

• Question 168:

  Which of the following actions can the eval command perform?

  A. Remove fields from results.
  B. Create or replace an existing field.
  C. Group transactions by one or more fields.
  D. Save SPL commands to be reused in other searches.
  B. Create or replace an existing field.

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  Explanation/Reference:

  The eval command is used to create new fields or modify existing fields based on an expression. The eval command can perform various actions such as calculations, conversions, string manipulations and more. One of the actions that the eval command can perform is to create or replace an existing field with a new value based on an expression. For example, | eval status=if(status="200","OK","ERROR") will create or replace the status field with either OK orERROR depending on the original value of status. Therefore, option B is correct, while options A, C and D are incorrect because they are not actions that the eval command can perform.

• Question 169:

  The macro weekly_sales (2) contains the search string:

  index--games I eval Product Sales = $price$ $AmountS01d$ Which of the following will return results?

  A. `weekly_sales(3.99, 10) '
  B. `weekly_sales($3.99$, $10$)
  C. 'weekly_sales (3.99, 10)
  D. `weekly_sales(3)
  C. 'weekly_sales (3.99, 10)

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  Explanation/Reference:

  The correct answer is C. `weekly_sales (3.99, 10)'. This is because search macros accept arguments without quotation marks or dollar signs, and the number of arguments must match the number of parameters defined in the macro. The other options are incorrect because they either use quotation marks or dollar signs around the arguments, or they provide a different number of arguments than the macro expects.You can learn more about how to use search macros in searches from the Splunk documentation1.

• Question 170:

  Which of the following is true about a datamodel that has been accelerated?

  A. They can be used with Pivot, the | tstats command, or the | datamodel command.
  B. They can still be used in the Pivot tool but only with the accelerate_pivot capability.
  C. They can no longer be used in the Pivot tool.
  D. They can be used with the |tstats command, but will only return that data which has been accelerated.
  A. They can be used with Pivot, the | tstats command, or the | datamodel command.

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  Explanation/Reference:

  A data model that has been accelerated can be used with Pivot, the | tstats command, or the | datamodel command (Option A). Acceleration pre-computes and stores results for quicker access, enhancing the performance of searches and analyses that utilize the data model, especially for large datasets. This makes accelerated data models highly efficient for use in various analytical tools and commands within Splunk.