With Azure OpenAI , you don't call a raw "model name" directly; you call a deployment of that model.
SDKs and REST require:
the resource endpoint ,
the API key (or Entra ID token), and
the deployment name to target the specific model instance.
References
Azure OpenAI quickstart (connect using endpoint, key, and deployment name ).
https://learn.microsoft.com/azure/ai-services/openai/quickstart
Chat/completions-path uses /deployments/{deployment-id}.
https://learn.microsoft.com/azure/ai-services/openai/reference#chat-completions

This is because the Azure Video Indexer website allows you to upload videos from a URL or from your file system, but there are some limitations and considerations for each option1.
If you upload from your file system, the size of the file is limited to 2 GB, which is less than the size of file1.
avi (20 GB). Therefore, this option is not feasible.
If you upload from a URL, the size of the file is limited to 30 GB, which is enough for file1.avi. However, the URL must be publicly accessible and valid, and the file must be accessible. You cannot use URLs from streaming services such as YouTube1. Therefore, options A and B are not valid.
The best option is to upload file1.avi to a cloud storage service such as Microsoft OneDrive, and then generate a shareable link for the file. You can then paste the link in the Azure Video Indexer website and upload the video from the URL. This way, you can avoid the file size limitation and ensure that the file is accessible and valid2.

When working with Speech Studio in Azure AI Speech (for example, to build a custom speech model for recognition or training purposes), you must provide speech samples along with their transcripts.
Requirements for Uploading Speech Samples:
Audio File Format
The supported format is .wav (PCM, 16-bit, 16 kHz or 8 kHz mono).
Other formats like .wma , .flac , or .mp3 are not accepted for training custom models.
Batch Upload via .zip
Audio files must be placed in a .zip archive.
The .zip file must also include a transcription file (usually a .txt or .json file) that maps the spoken audio in each .wav file to the correct text.
Transcript Format
Transcripts must be plain text, not Microsoft Word documents.
The transcript must align correctly with each audio file to ensure the Speech service learns from accurate mappings.
Option Analysis
A). Combine into one .wma file
Incorrect. .wma is not supported. Also, Speech Studio expects multiple .wav samples, not one big file.
B). Upload a .zip file containing .wav files + transcript
Correct. This is the officially supported method for uploading training data into a Speech Studio project.
C). Upload .flac files with Word transcript
Incorrect. .flac is not supported. Transcripts must be plain text, not Word files.
D). Upload individual .wma files
Incorrect. .wma is not supported.
The answer: B. Upload a .zip file that contains a collection of audio files in the .wav format and a corresponding text transcript file.
Microsoft References
Prepare training data for Custom Speech
Speech Studio - Data Requirements

Comprehensive Detailed Explanation
You are working with a custom question answering project in Azure AI Language (formerly QnA Maker).
The requirement is to enable the chatbot to engage in multi-turn conversations.
Multi-turn conversation: This occurs when a bot needs to ask clarifying questions or present follow-up options to guide the user toward the right answer.
Now let's evaluate the options:
A). Add alternate questions
Alternate questions help improve query matching by allowing different phrasings of the same question.
This does not enable multi-turn dialogue.
B). Enable chit-chat
Chit-chat adds conversational personality (e.g., greetings, small talk).
Useful for user engagement but not for structured multi-turn dialogues.
C). Add follow-up prompts #
Follow-up prompts allow you to define next-step questions after an answer.
They are the key mechanism in custom question answering to enable multi-turn conversation flows.
Example: If the answer is about " subscriptions, " the follow-up prompts might include " How to upgrade? " or " How to cancel? " D). Enable active learning Active learning helps improve accuracy by suggesting alternative phrasing for questions based on user feedback.
This is for model refinement, not multi-turn conversation.
Thus, the correct way to configure the project for multi-turn conversations is to add follow-up prompts.
The answer: C. Add follow-up prompts
Microsoft References
Custom question answering in Azure AI Language - Multi-turn conversations Custom question answering overview

For Azure OpenAI fine-tuning, training data must be provided as a JSON Lines (.jsonl) file. Each line represents one example. Because your data is described as prompt-completion pairs, the required shape is typically:
{ " prompt " : " < your prompt > " , " completion " : " < ideal completion > " } (Chat model fine-tuning instead uses a messages array per line, but the file format is still JSONL.) JSONL is the only accepted format for upload to the fine-tuning job.
Microsoft References
Azure OpenAI Service - Fine-tuning: data preparation and formats (JSONL; prompt-completion and chat messages ).
Azure OpenAI Service - How to fine-tune models (training files must be .jsonl).