# Working with Amazon Quick Sight Topics | | | --- | |  Applies to: Enterprise Edition  | | | | --- | |    Intended audience: Amazon Quick administrators and authors  | *Topics* are collections of one or more datasets that represent a subject area that your business users can ask questions about. With Quick Sight automated data prep, you get an ML-powered assist to help you create a topic that is relevant to your end users. The first process begins with automated field selection and classification, something like this: + Automated data prep chooses a small number of fields to include by default to create a focused data space for readers to explore. + Automated data prep selects fields that you use in other assets like reports and dashboards. + Automated data prep also imports any additional fields from any related analysis where a topic is enabled. + It identifies dates, dimensions, and measures, to learn how fields can be used in answers. This automatic set of fields help the author quickly get started with natural language analytics. Authors can always exclude fields, or include additional fields, as needed by using the **Include** toggle. Next, automated data prep continues with the process by automatically labeling fields and identifying synonyms. Automated data prep updates field names with friendly names and synonyms using common terms. For example, a `SLS_PERSON` field might be renamed to `Sales person`, and assigned synonyms including: `salesman`, `saleswoman`, agent, and `sales representative`. Although you can let automated data prep do much of the work, it's worthwhile to review the fields, names, and synonyms to further customize them for your end users. For example, if the users refer to a sales person as a "rep" or a "dealer" in casual conversation, then you support this term by adding `rep` and `dealer` to the synonyms for `SLS_PERSON`. Finally, automated data prep detects the semantic type of each field, by sampling its data and examining the formats applied to it by the author during analysis. Automated data prep updates the field configuration automatically, setting formats for values used for each field. Answers to questions are thus provided in expected formats for dates, currencies, identifiers, Booleans, persons, and so on. To learn more about working with topics, continue on to the following sections in this chapter. **Topics** + [Navigating Topics](navigating-topics.md) + [Creating Quick Sight topics](topics-create.md) + [Topic workspace](topics-interface.md) + [Working with datasets in an Quick Sight topic](topics-data.md) + [Making Quick Sight topics natural-language-friendly](topics-natural-language.md) + [Sharing Quick Sight topics](topics-sharing.md) + [Managing Amazon Quick Sight topic permissions](topics-sharing-permissions.md) + [Reviewing Quick Sight topic performance and feedback](topics-performance.md) + [Refreshing Quick Sight topic indexes](topics-index.md) + [Work with Quick Sight topics using the Amazon Quick Sight APIs](topics-cli.md)