Data model attributes are fields that are added to a dataset from search time field extractions, calculated fields, lookups, or aliases. They can be created within the data model editor or inherited from a parent dataset. They can be edited or removed unless they are required by the data model. They can be added to any type of dataset, not just root search datasets.ReferencesSee About data models, [Define data model attributes], and [Edit data model datasets] in the Splunk Documentation.

Explanation:
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 outfrom 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.

Explanation:
Thefillnullcommand in Splunk is used to handle missing data within search results. It plays a crucial role in data normalization and preparation, especially before performing statistical analyses or visualizations.
A.Replace empty values with a specified value:This is the correct answer. Thefillnull command is specifically designed to replace null values (empty values) with a specified default value. This is particularly useful in ensuring consistency within your data, especially when performing operations that require numerical values or when you want to distinguish between genuinely missing data and zeroes, for instance.
Example Usage:... | fillnull value=0This command would replace all null values in the search results with 0.

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.

Explanation: In Splunk, when using thechartcommand, theuseotherparameter can be set tofalse(f) to remove the 'OTHER' category, which is a bucket that Splunk uses to aggregate low-cardinality groups into a single group to simplify visualization. Here's how the options break down:
A.| chart count over CurrentStanding by Action useother=fThis command correctly sets theuseotherparameter tofalse, which would prevent the 'OTHER' category from being displayed in the resulting visualization.
B.| chart count over CurrentStanding by Action usenull=f useother=tThis command hasuseotherset totrue(t), which means the 'OTHER' category would still be included, so this is not a correct option.
C.| chart count over CurrentStanding by Action limit=10 useother=fSimilar to option A, this command also setsuseothertofalse, additionally imposing a limit to the top 10 results, which is a way to control the granularity of the chart but also to remove the 'OTHER' category.
D.| chart count over CurrentStanding by Action limit-10This command has a syntax error (limit-10should belimit=10) and does not include theuseother=fclause. Therefore, it would not remove the 'OTHER' category, making it incorrect.
The correct answers to rewrite the syntax to remove the 'OTHER' category are options A and C, which explicitly setuseother=f.

Pivot is used for creating reports and dashboards. Pivot is a tool that allows you to create reports and dashboards from your data models without writing any SPL commands. Pivot can help you visualize and analyze your data using various options, such as filters, rows, columns,cells, charts, tables, maps, etc. Pivot can also help you accelerate your reports and dashboards by using summary data from your accelerated data models.
Pivot is not used for creating datasets or data models. Datasets are collections of events that represent your data in a structured and hierarchical way. Data models are predefined datasets for various domains, such as network traffic, web activity, authentication, etc. Datasets and data models can be created by using commands such as datamodel or pivot.

Explanation: The fields sidebar is a panel that shows the fields that are present in your search results2. The fields sidebar has two sections: selected fields and interesting fields2. Selected fields are fields that you choose to display in your search results by clicking on them in the fields sidebar or by using the fieldscommand2. Interesting fields are fields that appear in at least 20 percent of events or have high variability among values2. For each field in the fields sidebar, you can select one of the following options: events with this field, rare values, top values by time or top values2. If you select rare values, Splunk will automatically pipe your search results to the rare command, which shows the least common values of a field2. Therefore, option B is correct, while options A, C and D are incorrect because they do not pipe your search results to the rare command.