The requirement is to process images of forms with Azure Form Recognizer (now part of Azure AI Document Intelligence) while ensuring that the image files are not stored in the cloud.
When sending images directly to the Form Recognizer REST API endpoint, you have two options:
* Send the file as binary data in the request body (for local file upload).
* Send a URL reference (if the file is stored in Azure Blob Storage or another public location).
Since the requirement explicitly states that the image files must not be stored in the cloud, you cannot send a URL. Instead, you must send the raw binary content of the file directly in the request body.
* Option A - raw image binary
* Correct. This allows you to directly send the local image file to the API without uploading/storing it in the cloud.
* Option B - form URL encoded
* Incorrect. Form Recognizer does not accept URL-encoded form data.
* Option C - JSON
* Incorrect. JSON is only used when providing the API with the URL of a cloud-stored document, not when sending the actual file.
The answer: A. raw image binary
* Form Recognizer REST API - Analyze a document
* Quickstart: Extract layout and text - Document Intelligence"If your document is available locally, pass the file as binary data in the request body."

Your app currently calls the prebuilt Business Card model v2.1. In v2.1, the platform does not provide QR
/barcode interpretation. Support for decoding barcodes (including QR codes) was introduced with the v3.x API family as an add-on capability (ocr.barcode) that you can enable on analysis requests in API versions
2023-07-31 (GA) and later. Therefore, the first step is to upgrade from v2.1 to v3.0 so your application can use the newer API surface that supports barcode/QR extraction. This meets the "minimize administrative effort" requirement because you keep the same Azure AI Document Intelligence resource, keys, and endpoint-your change is primarily to the API/SDK version and request options.
After upgrading, you have two common implementation patterns:
* Continue to use prebuilt Business Card for the contact fields, and in the same flow make an additional call with prebuilt Layout (or General Document) with the ocr.barcode add-on enabled to decode any QR code present on the card. This is the recommended path because the Business Card model itself doesn't expose add-on features, while the platform's v3.x analysis APIs do support ocr.barcode on other models. Microsoft Learn Why the other options are not correct:
* A. Deploy a custom model - unnecessary effort; barcode/QR decoding is available out-of-the-box in v3.
x via ocr.barcode. Microsoft Learn
* B. Implement the read model - Read (OCR) extracts text but does not interpret barcodes/QR codes; you need the barcode add-on introduced in v3.x. Microsoft Learn
* D. Implement the contract model - Irrelevant to business cards or QR decoding.
Key Microsoft References
* Add-on capabilities (including ocr.barcode with QR support) - available in API 2023-07-31 (GA) and later. Microsoft Learn
* Barcode/QR extraction behavior and schema (barcodes collection, kind, value, polygon). Microsoft Learn
* Migration guide from v2.1 to v3.x (why upgrade and what changes). Azure Docs
* Business Card model documentation and versioning context (v2.1/v3.0/v3.1). Microsoft Learn
* SDK/blog announcement of barcode add-on in v3.x (what it returns and when it's supported). Microsoft for Developers
* Code samples showing the barcodes add-on usage. github.com

Explanation:
Semi-structured data
Let's break down the options:
* Graph data
* Used in graph databases (e.g., Azure Cosmos DB Gremlin API, Neo4j).
* Represents nodes and edges with relationships.
* JSON is not graph data.
* Relational data
* Stored in relational databases with tables, rows, and columns.
* JSON does not follow a fixed schema with rows/columns.
* Semi-structured data
* Correct.
* JSON, XML, and Avro are common examples of semi-structured data.
* They don't require a rigid schema like relational data, but still have organizational elements (tags, key-value pairs).
* JSON is widely used in NoSQL/document databases like Azure Cosmos DB.
* Unstructured data
* Examples: images, videos, free text, audio.
* JSON does not fit here because it has a defined structure with keys and values.
The answer: Semi-structured data
* Structured, semi-structured, and unstructured data
* What is semi-structured data?
* Azure Cosmos DB and JSON data model

* The interface must collect structured input: start date, end date, PTO days.
* A waterfall dialog is designed for step-by-step data gathering. Each step asks one question and collects one piece of information, minimizing complexity.
* Skill dialog is for invoking another bot.
* Adaptive dialog handles interruptions and dynamic flow (more complex).
* Component dialog organizes dialogs but does not simplify sequential data collection.
The answer: B
Reference: Waterfall dialogs in Bot Framework

The application stores data in a shared folder on a Windows server. The equivalent in Azure Storage is Azure Files, which provides:
* Fully managed file shares in the cloud.
* Support for the SMB (Server Message Block) protocol, so they can be mounted like traditional shared folders.
* Easy migration of on-premises file shares to Azure.
Other storage options:
* Table # NoSQL key-value store, not for file shares.
* Queue # For message storage and processing, not files.
* Blob # For unstructured object storage, but not mountable as a shared drive.
Therefore, Azure File Storage is correct.
Microsoft Reference: Azure Files overview