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

In Splunk, Pivot is a tool that allows you to report on a specific data set without using the Splunk Search Processing Language (SPL™)1. You can use a drag-and-drop interface to design and generate pivots that present different aspects of your data in the form of tables, charts, and other visualizations12.
One of the features of Pivot is that it allows you to save your reports1. This can be useful when you want to reuse a report or share it with others1. Therefore, it’s not true that users cannot share visualizations created with Pivot or that they must use SPL to find events in a Pivot12. It’s also not true that users cannot create visualizations with Pivot, as creating visualizations is one of the main functions of Pivot12.

To configure a Search Workflow Action to run at the same time range as the original search, you need to select the “Use the same time range as the search that created the field listing” checkbox. This will ensure that the workflow action search uses the same earliest and latest time parameters as the original search.

Explanation
To create a macro that accepts arguments, you must include the number of arguments in parentheses at the end of the macro name1. For example, my_macro(3) is a macro that accepts three arguments. The number of arguments in the macro name must match the number of arguments in the definition1. Therefore, option A is correct, while options B, C and D are incorrect.

Reference:https://docs.splunk.com/Documentation/Splunk/8.0.3/Search/Abouttransactions

In Splunk, calculated fields allow you to create new fields using expressions that can transform or combine the values of existing fields. Although all options provided might seem viable, when selecting only one option that is most representative of a calculated field, we typically refer to:
D: Extracted fields: Calculated fields are often based on fields that have already been extracted from your data. Extracted fields are those that Splunk has identified and pulled out from the event data based on patterns, delimiters, or other methods such as regular expressions or automatic extractions. These fields can then be used in expressions to create calculated fields.
For example, you might have an extracted field for the time in seconds, and you want to create a calculated field for the time in minutes. You would use the extracted field in a calculation to create the new field.
It’s important to note that although fields generated within a search string (A) and regular expressions (C) can also be used in the calculation of a new field, and lookup tables (B) can be used to enrich data, option D is typically what one refers to when discussing calculated fields, as it implies a direct transformation or calculation based on fields that have been extracted from the raw data.

Auto-Extracted fields in Splunk Data Models are derived directly from the indexed data based on the existing fields within the events. These fields are identified and extracted by Splunk automatically, without the need for explicit field extractions configured by the user. Understanding the characteristics of Auto-Extracted fields is crucial for effectively managing Data Models and utilizing them in Pivot tables for analysis.
A: Auto-Extracted fields can be hidden in Pivot. This is true. When building a Data Model, you have the option to hide certain fields from appearing in Pivot, making the Pivot table cleaner and more focused on the fields that are most relevant for analysis. This helps in reducing clutter and focusing on the data that matters most to the users.
B: Auto-Extracted fields can have their data type changed. This statement is not typically accurate for Auto-Extracted fields. The data type of an Auto-Extracted field is determined by Splunk based on the field’s content in the indexed data. While you can assign a type to a field when you manually create a field in a data model, the inherent data type of Auto-Extracted fields is not something that is changed within the Data Model itself.
C: Auto-Extracted fields can be given a friendly name for use in Pivot. This is correct. Within Data Models, you can assign a more user-friendly, descriptive name to an Auto-Extracted field. This feature is particularly useful in making Data Models more intuitive and easier to use for those who may not be familiar with the original field names or when the original field names are not descriptive or user-friendly.
D: Auto-Extracted fields can be added if they already exist in the dataset with constraints. This is true.
Auto-Extracted fields are based on fields that already exist in the data. When you define a dataset within a Data Model, you can apply constraints to narrow down the events that the dataset includes. The Auto-Extracted fields are then identified from this constrained dataset. This means that the fields must already be present in the data that meets the dataset’s constraints to be available for auto-extraction.
In summary, Auto-Extracted fields in Splunk Data Models offer a flexible and efficient way to utilize existing data fields within Pivot tables, with options to rename them for clarity and hide unnecessary fields to streamline data analysis.

Explanation
Explanation/Reference: https://docs.splunk.com/Documentation/Splunk/8.1.1/Data/Extractfieldsfromfileswithstructureddata

The transaction command is used to group events into transactions based on some common characteristics,
such as fields, time, or both. The transaction command can also specify start and end constraints for the
transactions, such as a field value that indicates the beginning or the end of a transaction. The stats command
is used to calculate summary statistics on the events, such as count, sum, average, etc. The stats command
cannot group events based on start and end constraints, but only on fields or time buckets. Therefore, the
transaction command should be used instead of the stats command when you need to group events based on
start and end constraints.

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

The correct answer is B. Transforming.
The explanation is as follows:
* The timechart command is a Splunk command that creates a time series chart with corresponding table of statistics12.
* A timechart is a statistical aggregation applied to a field to produce a chart, with time used as the X-axis1. You can specify a split-by field, where each distinct value of the split-by field becomes a series in the chart1.
* Transforming commands are commands that change the format of the search results into a data structure that can be easily visualized3. Transforming commands often use stats functions to aggregate and summarize data3.
* Therefore, the timechart command is an example of a transforming command, as it transforms the search
* results into a chart and a table using stats functions123.

Explanation
A field alias is a way to assign an alternative name to an existing field without changing the original field name or value2. You can use field aliases to make your field names more consistent or descriptive across different sources or sourcetypes2. When you run a search without any transforming commands in Smart Mode, Splunk automatically identifies and displays interesting fields in your results2. Interesting fields are fields that appear in at least 20 percent of events or have high variability among values2. If you have created a field alias based on an original field, both the original field name and the alias name will appear in the Interesting Fields list if they meet these criteria2. However, only one of them will appear in each event depending on which one you have specified in your search string2. Therefore, option B is correct, while options A, C and D are incorrect.

Reference:https://docs.splunk.com/Documentation/PCI/4.1.0/Install/PrivilegedUserActivity
A tag is a descriptive label that you can apply to one or more fields or field values in your events1. You can
use tags to simplify your searches by replacing long or complex field names or values with short and simple
tags1. To search for events that contain a tag name, you can use the tag keyword followed by an equal sign
and the tag name1. You can also use wildcards (*) to match partial tag names1. Therefore, option B is correct
because it will return events that contain a tag name that starts with Pri. Options A and D are incorrect because
they will only return events that contain an exact tag name match. Option C is incorrect because it will return
events that contain a tag name that starts with Priv, not Privileged.