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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Creating and Using Workflow Actions | 10% | - Use workflow actions to extend searches - Describe GET, POST, and Search workflow actions - Create and configure workflow actions |
| Topic 2: Correlating Events | 15% | - Identify and use transactions - Compare transactions vs stats commands - Group events by fields and time |
| Topic 3: Using the Common Information Model (CIM) Add-On | 5% | - Describe Splunk CIM purpose and structure - Normalize data using CIM knowledge objects - Use CIM to standardize data across sources |
| Topic 4: Transforming Commands and Visualizations | 15% | - Format results for presentation - Use transforming commands to structure data - Create and customize visualizations |
| Topic 5: Using Macros | 10% | - Add and use arguments in macros - Manage macro permissions and sharing - Create and reuse search macros |
| Topic 6: Creating Tags and Event Types | 10% | - Use tags and event types in searches - Define event types to categorize events - Create and apply tags to fields or values |
| Topic 7: Creating Data Models | 10% | - Define data model objects and attributes - Understand data models and Pivot - Create and use data models |
| Topic 8: Filtering and Formatting Results | 15% | - Sort, rename, and limit results - Use search and where commands - Use fillnull, eval, and other formatting commands |
| Topic 9: Creating and Using Field Aliases and Calculated Fields | 10% | - Define and use field aliases - Manage field extractions and aliases - Create calculated fields with eval |
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NEW QUESTION # 10
In the Field Extractor Utility, this button will display events that do not contain extracted fields.
Select your answer.
Answer: A
NEW QUESTION # 11
When you mouse over and click to add a search term this (thesE. Boolean operator(s) is(arE. not implied.
(Select all that apply).
Answer: B,C,D
Explanation:
When you mouse over and click to add a search term from the Fields sidebar or from an event in your search
results, Splunk automatically adds the term to your search string with an implied ANDoperator2. However,
this does not apply to some Boolean operators such as OR, NOT and parentheses (). These operators are not
implied when you add a search term and you have to type them manually if you want to use them in your
search string2. Therefore, options A, B and D are correct, while option C is incorrect because AND is implied
when you add a search term.
NEW QUESTION # 12
A calculated field is a shortcut for performing repetitive, long, or complex transformations using which of the
following commands?
Answer: A
Explanation:
The correct answer is D. eval.
A calculated field is a field that is added to events at search time by using an eval expression. A calculated
field can use the values of two or more fields that are already present in the events to perform calculations. A
calculated field can be defined with Splunk Web or in the props.conf file.They can be used in searches,
reports, dashboards, and data models like any other extracted field1.
A calculated field is a shortcut for performing repetitive, long, or complex transformations using the eval
command. The eval command is used to create or modify fields by using expressions.The eval command can
perform mathematical, string, date and time, comparison, logical, and other operations on fields or values2.
For example, if you want to create a new field named total that is the sum of two fields named price and tax,
you can use the eval command as follows:
| eval total=price+tax
However, if you want to use this new field in multiple searches, reports, or dashboards, you can create a
calculated field instead of writing the eval command every time. To create a calculated field with Splunk Web,
you need to go to Settings > Fields > Calculated Fields and enter the name of the new field (total), the name of
the sourcetype (sales), and the eval expression (price+tax). This will create a calculated field named total that
will be added to all events with the sourcetype sales at search time.You can then use the total field like any
other extracted field without writing the eval expression1.
The other options are not correct because they are not related to calculated fields. These options are:
A: transaction: This command is used to group events that share some common values into a single
record, called a transaction.A transaction can span multiple events and multiple sources, and can be
useful for correlating events that are related but not contiguous3.
B: lookup: This command is used to enrich events with additional fields from an external source, such as
a CSV file or a database. A lookup can add fields to events based on the values of existing fields, such
as host, source, sourcetype, or any other extracted field.
C: stats: This command is used to calculate 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.
References:
About calculated fields
eval command overview
transaction command overview
[lookup command overview]
[stats command overview]
NEW QUESTION # 13
Data model fields can be added using the Auto-Extracted method. Which of the following statements describe
Auto-Extracted fields? (select all that apply)
Answer: A,B,C,D
Explanation:
Data model fields are fields that describe the attributes of a dataset in a data model2. Data model fields can be
added using various methods such as Auto-Extracted, Evaluated or Lookup2. Auto-Extracted fields are fields
that are automatically extracted from your raw data using various techniques such as regular expressions,
delimiters or key-value pairs2. Auto-Extracted fields can be hidden in Pivot, which means that you can choose
whether to display them or not in the Pivot interface2. Therefore, option A is correct. Auto-Extracted fields
can have their data type changed, which means that you can specify whether they are strings, numbers,
booleans or timestamps2. Therefore, option B is correct. Auto-Extracted fields can be given a friendly name
for use in Pivot, which means that you can assign an alternative name to them that is more descriptive or
user-friendly than the original field name2. Therefore, option C is correct. Auto-Extracted fields can be added
if they already exist in the dataset with constraints, which means that you can include them in your data model
even if they are already extracted from your raw data by applying filters or constraints to limit the scope of
your dataset2. Therefore, option D is correct.
NEW QUESTION # 14
When would a user select delimited field extractions using the Field Extractor (FX)?
Answer: C
Explanation:
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
NEW QUESTION # 15
......
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