Explanation:

Comprehensive Detailed Explanation
The scenario:
You are working with Azure AI Video Indexer.
You need to add a custom brand/logo and also configure an exclusion so that this brand is not indexed.
In Video Indexer's REST API for brands, the configuration JSON includes:
" enabled " : true/false # Determines whether the brand should be indexed.
Setting " enabled " : false means the brand is excluded.
The other properties in the dropdown ( state , tags , useBuiltin ) are not correct in this context for configuring exclusions.
Therefore, to exclude a custom brand:
" enabled " : false
The Answer:
Attribute: " enabled "
Value: false
Microsoft References
Video Indexer - Brands customization
Azure Video Indexer API - Brands

According to the Microsoft documentation, Computer Vision is a cloud-based service that provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Computer Vision algorithms can analyze visual content in different ways based on inputs and user choices.
According to the Microsoft documentation, image type detection is one of the features of Computer Vision that can categorize an image as either a photograph or a drawing. You can use the image type detection feature by calling the Analyze Image API with the visualFeatures parameter set to ImageType. The API will return a JSON response with an imageType field that indicates whether the image is a photo or a clipart.
According to the Microsoft documentation, image descriptions is another feature of Computer Vision that can generate a caption for an image. You can use the image descriptions feature by calling the Analyze Image API with the visualFeatures parameter set to Description. The API will return a JSON response with a description field that contains a list of captions for the image, each with a confidence score.
Therefore, by using these two features of Computer Vision, you can achieve your app requirements with minimal development effort. You don't need to use any other services, such as object detection, content tags, or Custom Vision, which are designed for different purposes.

Explanation:

Explanation:

Comprehensive Detailed Explanation
To move a Question Answering project to a different Azure region, you migrate the project data and then re- deploy it in the target region:
Export the existing project (source region).
In Language Studio (Question Answering), you first export the project. This produces a package (JSON/ZIP) containing your knowledge bases, sources, synonyms, and settings. Exporting is the supported way to move a project between resources/regions.
Import the project into the new Language service instance (target region).
In the target region's Language service instance, use Language Studio to import the previously exported file.
This recreates the project (KBs and configuration) under the new resource.
Train and publish from the new instance.
After import, you must train (build) and then publish the project so that an endpoint is created under the new regional resource. Clients can then point to the new endpoint and key.
Actions like "enable custom text classification" or "regenerate the keys" are not required for moving a Question Answering project, and training/publishing from the original instance won't deploy to the target region.
Microsoft Azure AI Solution References
Export and import Question Answering projects in Language Studio (move between resources/regions):
Microsoft Learn - Export and import projects (Question Answering) .
https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/export-import- project Train and publish a Question Answering project:
Microsoft Learn - Create, train, and publish projects (Question Answering) .
https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/create-test-deploy Language service overview and regional deployment concepts:
Microsoft Learn - What is Azure AI Language?
https://learn.microsoft.com/azure/ai-services/language-service/overview

Azure AI Video Indexer (VI) imposes limits on both file duration and file size:
Duration: Up to 6 hours for all presets; Basic Audio preset supports up to 12 hours.
Size: When uploading from your device, the limit is 2 GB (uploading by URL allows up to 30 GB).
Formats: MP4, AVI (video) and MP3 (audio codec/file) are supported.
Evaluating each file:
File1 - MP4, 34 mins, 1,500 MB: Within 6-hour duration and under 2 GB # Supported.
File2 - AVI, 500 mins (#8h20m), 1,700 MB: Exceeds the 6-hour limit # Not supported.
File3 - MP3, 300 mins (5h), 980 MB: Under 6 hours (and also under the 12-hour Basic Audio limit) and under 2 GB # Supported.
File4 - MP4, 350 mins (#5h50m), 2,800 MB: Duration is under 6 hours but size exceeds 2 GB for device uploads # Not supported (unless uploaded by URL, which the question doesn't state).
Therefore, the files you can upload and analyze (under standard device upload constraints) are File1 and File3 only.
Microsoft References
Support matrix & limits - file duration (6h for all presets; 12h for Basic Audio), device upload size limit 2 GB; supported formats (MP4, AVI, WAV, etc.) and audio codecs including MP3. Microsoft Learn Release notes - increase of duration limit from 4h to 6h (and 12h for Basic Audio). Microsoft Learn Upload guidance - device upload 2 GB vs 30 GB via URL.