Reference: https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/WhatisSplunkknowledge Knowledge objects are entities that you create to add knowledge to your data and make it easier to search and analyze2. Some examples of knowledge objects are field extractions, field aliases and lookups2. Field extractions are methods that extract fields from your raw data using various techniques such as regular expressions, delimiters or key-value pairs2. Field aliases are ways to assign alternative names to existing fields without changing the original field names or values2. Lookups are ways to enrich your data with additional information from external sources such as CSV files or databases2. The order in which these knowledge objects/configurations are applied is as follows: field extractions, field aliases and then lookups2. This means that Splunk first extracts fields from your raw data, then applies any aliases to the extracted fields and then performs any lookups on the aliased fields2. Therefore, option B is correct, while options A, C and D are incorrect.

The Field Extractor (FX) is a tool that helps you extract fields from your events using a graphical interface2. You can create a report using a custom field extracted by the FX and share it with other users in your organization2. However, if another user runs the shared report and no results are returned, there could be two possible reasons. One reason is that the extraction is private, which means that only you can see and use the extracted field2. To make the extraction available to other users, you need to make it global or app-level2.
Therefore, option C is correct. Another reason is that the other user does not have access to the index where the events are stored2. To fix this issue, you need to grant the appropriate permissions to the other user for the index2. Therefore, option D is correct. Options A and B are incorrect because they are not related to the field extraction or the report.

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.

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.