2024 Realistic SPLK-1002 100% Pass Guaranteed Download Exam Q&A [Q11-Q29]

Explanation
The search string below returns events from the data model named Application_State.
| datamodel Application_State All_Application_State search
The search string does the following:
It uses the datamodel command to access a data model in Splunk. The datamodel command takes two arguments: the name of the data model and the name of the dataset within the data model.
It specifies the name of the data model as Application_State. This is a predefined data model in Splunk that contains information about web applications.
It specifies the name of the dataset as All_Application_State. This is a root dataset in the data model that contains all events from all child datasets.
It uses the search command to filter and transform the events from the dataset. The search command can use any search criteria or command to modify the results.
Therefore, the search string returns events from the data model named Application_State.

Explanation
The correct answer is B. The transaction command has a limit of 1000 events per transaction.
The transaction 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 contiguous1.
However, the transaction command has some limitations, one of which is that it can only group up to 1000 events per transaction. This means that if there are more than 1000 events that match the criteria for a transaction, they will be split into multiple transactions. This can result in incomplete or inaccurate transactions2.
To avoid this limitation, you can use the stats command instead of the transaction command. The stats command can also group events by common values, but it does not have a limit on the number of events per group. The stats command also performs faster and consumes less memory than the transaction command1.
In your search, you are using the stats list() function to group events by src_ip and dest_ip. This function returns a multivalue field that contains all the values of a given field for each group. However, this function does not create a single correlated event like the transaction command does. Instead, it creates a table of results with one row per group and one column per field3.
Therefore, your search will produce multiple transactions instead of one because you are using the transaction command with a limit of 1000 events per transaction, and you are using the stats list() function that does not create a single correlated event.
References:
stats command overview
transaction command overview
Splunk Transaction Command: What It Is and How to Use It
Splunk Core Certified Power User SPLK-1002 Practice Exam Part 1

Explanation
The correct answer is B. Use the field values in an HTTP error event to create a new ticket in an external system.
A workflow action is a knowledge object that enables a variety of interactions between fields in events and other web resources. Workflow actions can create HTML links, generate HTTP POST requests, or launch secondary searches based on field values1.
There are three types of workflow actions that can be set up using Splunk Web: GET, POST, and Search2.
GET workflow actions create typical HTML links to do things like perform Google searches on specific values or run domain name queries against external WHOIS databases2.
POST workflow actions generate an HTTP POST request to a specified URI. This action type enables you to do things like creating entries in external issue management systems using a set of relevant field values2.
Search workflow actions launch secondary searches that use specific field values from an event, such as a search that looks for the occurrence of specific combinations of ipaddress and http_status field values in your index over a specific time range2.
Therefore, the example that would use a POST workflow action is B. Use the field values in an HTTP error event to create a new ticket in an external system. This example requires sending an HTTP POST request to the URI of the external system with the field values from the event as arguments.
The other examples would use different types of workflow actions. These examples are:
A: Perform an external IP lookup based on a domain value found in events: This example would use a GET workflow action to create a link to an external IP lookup service with the domain value as a parameter.
C: Launch secondary Splunk searches that use one or more field values from selected events: This example would use a Search workflow action to run another Splunk search with the field values from the event as search terms.
D: Open a web browser to look up an HTTP status code: This example would also use a GET workflow action to create a link to a web page that explains the meaning of the HTTP status code.
References:
Splexicon:Workflowaction
About workflow actions in Splunk Web

This search uses the transaction command to group events that share a common value for JSESSIONID into transactions1. The transaction command assigns a duration field to each transaction, which is the difference between the latest and earliest timestamps of the events in the transaction1. The search then uses the timechart command to create a time-series chart of the average duration of each transaction1. Therefore, option A is correct because it describes the search accurately. Option B is incorrect because the search does not use the stats command or the pause field. Option C is incorrect because the transaction command does not require the startswith and endswith options, although they can be used to specify how to identify the beginning and end of a transaction1. Option D is incorrect because the transaction command does not have to be the last command in the search pipeline, although it is often used near the end of a search1.

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

Reference: https://docs.splunk.com/Documentation/Splunk/8.0.4/SearchReference/Timechart

Explanation/Reference: https://docs.splunk.com/Documentation/PCI/4.1.0/Install/PrivilegedUserActivity

The command sourcetype=access_combined | transaction JSESSIONID does three things:
It filters the events by the sourcetype access_combined, which is a predefined sourcetype for Apache web server logs.
It groups the events by the field JSESSIONID, which is a unique identifier for each user session.
It creates a single event from each group of events that share the same JSESSIONID value. This single event will have some additional fields created by the transaction command, such as duration, eventcount, and startime.
Therefore, the statements B, C, and D are true.

Explanation/Reference: https://docs.splunk.com/Documentation/Splunk/8.0.4/SearchReference/Eval

Reference:
https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/Usesearchmacros

Explanation/Reference: https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/WhatisSplunkknowledge

Reference:
https://answers.splunk.com/answers/574643/field-showing-an-additional-and-not-visible-value-1.html To use a macro in a search, you must enclose the macro name and any arguments in single quotation marks1.
For example, ‘my_macro(arg1,arg2)’ is a valid way to use a macro with two arguments. You can use macros anywhere in your search string where you would normally use a search command or expression1. Therefore, options A and C are valid searches that use macros, while options B and D are invalid because they do not enclose the macros in single quotation marks.

The fillnull command replaces null values with 0 by default, if the value argument is not specified. You can use the value argument to specify a different value to replace null values with, such as N/A or NULL.

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.
Reference:
Build field extractions with the field extractor
Configure indexed field extraction