Data model datasets have a hierarchical relationship with each other, meaning they have parent-child relationships. Data models can contain multiple dataset hierarchies. There are three types of dataset hierarchies: event, search, and transaction.
https://docs.splunk.com/Splexicon:Datamodeldataset

Reference: https://docs.splunk.com/Documentation/Splunk/8.0.3/SearchReference/Transaction One of the statements that would help a user choose between the transaction and stats commands is that there is a 1000 event limitation with the transaction command3.
The transaction command is used to group events that share a common value for one or more fields into transactions3. The transaction command has a default limit of 1000 events per transaction, which means that it will not group more than 1000 events into a single transaction3. This limit can be changed by using the maxevents parameter, but it can affect the performance and memory usage of Splunk3. Therefore, option C is correct, while options A, B and D are incorrect because they are not statements that would help a user choose between the transaction and stats commands.

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.

A comparison operator is a symbol that compares two values and returns a Boolean result (true or false)
2. Splunk supports various comparison operators such as <, >, =, !=, <=, >=, IN and LIKE2. However, ?= is not a valid comparison operator in Splunk and will cause a syntax error if used in a search string2. Therefore, option E is correct, while options A, B, C and D are incorrect because they are valid comparison operators in Splunk

When using multiple expressions in a single eval command in Splunk, the delimiter used is a comma (,). This allows for the execution of multiple operations within a single eval statement, separating each operation clearly.
References:
* Splunk Docs: Eval command
* Splunk Answers: Multiple expressions in eval