HOTSPOT -
You are creating an enrichment pipeline that will use Azure Cognitive Search. The knowledge store contains unstructured JSON data and scanned PDF documents that contain text.
Which projection type should you use for each data type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
Box 1: Object projection -
Object projections are JSON representations of the enrichment tree that can be sourced from any node.
Box 2: File projection -
File projections are similar to object projections and only act on the normalized_images collection.
Reference:
https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview
Correct!
1. Object 2. File https://learn.microsoft.com/en-us/azure/search/knowledge-store-projections-examples#define-an-object-projection Object projections are JSON representations of the enrichment tree that can be sourced from any node. In comparison with table projections, object projections are simpler to define and are used when projecting whole documents. Object projections are limited to a single projection in a container and can't be sliced. https://learn.microsoft.com/en-us/azure/search/knowledge-store-projections-examples#define-a-file-projection File projections are always binary, normalized images, where normalization refers to potential resizing and rotation for use in skillset execution. File projections, similar to object projections, are created as blobs in Azure Storage, and contain binary data (as opposed to JSON).
Correct answer. https://learn.microsoft.com/en-us/azure/search/knowledge-store-projections-examples
java script OBJECT notation (JSON)
Answer 1: Object Projection: 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. Answer 2: File Projection: Used when you need to save normalized, binary image files, eg: Optical Character Recognition (OCR) extracts text from binary files Not Answer: Table Projection: 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.
1. Object 2. File
1. Object 2. File
we are dealing with unstructured JSON data here. Due to the potential irregularities in the data it will probably not be feasible using table projection. Using object projection is simpler and sometimes the only choice depending on the nature of the data.
Object File https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview#types-of-projections-and-usage
Types of projections and usage A knowledge store is a logical construction that's physically expressed as a loose collection of tables, JSON objects, or binary image files in Azure Storage. TYPES OF PROJECTIONS AND USAGE Projection Storage Usage Tables Azure Table Storage 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. You can organize content into multiple tables based on familiar normalization principles. Tables that are in the same group are automatically related. Objects Azure Blob Storage 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. Files Azure Blob Storage Used when you need to save normalized, binary image files.