You have an Azure Cognitive Search instance that indexes purchase orders by using Form Recognizer. You need to analyze the extracted information by using Micros...

Projections are the physical tables, objects, and files in a knowledge store that accept content from a Cognitive Search AI enrichment pipeline. If you're creating a knowledge store, defining and shaping projections is most of the work.
Objects is used when you need the full JSON representation of your data and enrichments in one JSON document. As with table projections, only valid JSON objects can be projected as objects, and shaping can help you do that.
Note: Form Recognizer analyzes your forms and documents, extracts text and data, maps field relationships as key-value pairs, and returns a structured JSON output. You quickly get accurate results that are tailored to your specific content without excessive manual intervention or extensive data science expertise.
Incorrect:
Not Tables: Tables is used for data that's best represented as rows and columns, or whenever you need granular representations of your data (for example, as data frames). Table projections allow you to define a schematized shape, using a Shaper skill or use inline shaping to specify columns and rows.
Not File: File is used when you need to save normalized, binary image files.
Reference:
https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview