Topic 2: Questions Set 2
Which of the following statements describe data model acceleration? (select all that apply)
A. Root events cannot be accelerated.
B. Accelerated data models cannot be edited.
C. Private data models cannot be accelerated.
D. You must have administrative permissions or the accelerate_dacamodel capability to accelerate a data model.
Explanation: 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.
Which group of users would most likely use pivots?
A. Users
B. Architects
C. Administrators
D. Knowledge Managers
A pivot is a tool that allows you to create reports and dashboards using data models without writing any SPL commands2. You can use pivots to explore, filter, split and visualize your data using a graphical interface2. 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 data2. 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.
After manually editing; a regular expression (regex), which of the following statements is true?
A. Changes made manually can be reverted in the Field Extractor (FX) UI.
B. It is no longer possible to edit the field extraction in the Field Extractor (FX) UI.
C. It is not possible to manually edit a regular expression (regex) that was created using the Field Extractor (FX) UI.
D. The Field Extractor (FX) UI keeps its own version of the field extraction in addition to the one that was manually edited.
Explanation: 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 that helps 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.
What functionality does the Splunk Common Information Model (CIM) rely on to normalize fields with different names?
A. Macros.
B. Field aliases.
C. The rename command.
D. CIM does not work with different names for the same field.
Explanation: 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.
What does the fillnull command replace null values with, it the value argument is not specified?
A. 0
B. N/A
C. NaN
D. NULL
The fillnull command is a search command that replaces null values with a specified value or 0 if no value is specified. Null values are values that are missing, empty, or undefined in Splunk. The fillnull command can replace null values for all fields or for specific fields. The fillnull command can take an optional argument called value that specifies the value to replace null values with. If no value argument is specified, the fillnull command will replace null values with 0 by default.
In which of the following scenarios is an event type more effective than a saved search?
A. When a search should always include the same time range.
B. When a search needs to be added to other users' dashboards.
C. When the search string needs to be used in future searches.
D. When formatting needs to be included with the search string.
An event type is a way to categorize events based on a search string that matches the events2. You can use event types to simplify your searches by replacing long or complex search strings with short and simple event type names2. An event type is more effective than a saved search when the search string needs to be used in future searches because it allows you to reuse the search string without having to remember or type it again2. Therefore, option C is correct, while options A, B and D are incorrect because they are not scenarios where an event type is more effective than a saved search.
Based on the macro definition shown below, what is the correct way to execute the macro in a search string?
A. Convert_sales (euro, €, 79)”
B. Convert_sales (euro, €, .79)
C. Convert_sales ($euro,$€$,s79$
D. Convert_sales ($euro, $€$,S,79$)
The correct way to execute the macro in a search string is to use the format macro_name($arg1$, $arg2$, ...) where $arg1$, $arg2$, etc. are the arguments for the macro. In this case, the macro name is convert_sales and it takes three arguments: currency, symbol, and rate. The arguments are enclosed in dollar signs and separated by commas. Therefore, the correct way to execute the macro is convert_sales($euro$, $€$, .79).
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