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NEW QUESTION # 106
You are developing an application that analyzes uploaded video files by using Azure Content Understanding in Foundry Tools.
You need to ensure that the application extracts structured information from the videos.
What should the application call?
Answer: A
NEW QUESTION # 107
What can be used to analyze scanned invoices and extract data, such as billing addresses and the total amount due?
Answer: D
Explanation:
The Document Intelligence invoice model uses powerful Optical Character Recognition (OCR) capabilities to analyze and extract key fields and line items from sales invoices, utility bills, and purchase orders.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/prebuilt/invoice
NEW QUESTION # 108
You are developing an application that analyzes voicemail recordings by using Azure Content Understanding in Foundry Tools.
You need to extract a transcript and structured information from the recordings.
Which type of analyzer should you use?
Answer: D
Explanation:
Voicemail recordings are audio content. Azure Content Understanding analyzers define what type of content to process, including documents, images, audio, or video, and what elements to extract, including transcripts and structured fields.
Microsoft's custom analyzer documentation also shows an audio example based on prebuilt-audio for processing customer support call recordings, which is the same content type as voicemail recordings.
Therefore, to extract a transcript and structured information from voicemail recordings, you should use an audio analyzer.
NEW QUESTION # 109
You are developing a simple application that uses the Microsoft Foundry SDK to send chat prompts to a model.
Which three elements should you provide in the code to run the application? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C,E
Explanation:
To build a simple application that sends chat prompts to a model using the Microsoft Foundry SDK, you will initialize a project client using your endpoint and credentials, then request chat completions using your model deployment name.
Microsoft Entra ID Credentials: Handled via DefaultAzureCredential(). This eliminates the need to hardcode sensitive API keys. Before running this app, ensure you are authenticated in your terminal by running az login via the Azure CLI. Your account must have the Cognitive Services User or Azure AI Developer role assigned.
Endpoint URL: Passed directly into the AIProjectClient initializer. This maps your code to the specific Azure AI Foundry project hub managing your computational resources.
Model Deployment Name: Referenced inside the final .create() method payload. This instructs the inference routing service exactly which deployed LLM (e.g., GPT-4o, DeepSeek, or Claude) should process your incoming chat transcript.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/chatgpt
NEW QUESTION # 110
You are building a chatbot that will use natural language processing (NLP) to perform the following actions based on the text input of a user:
* Accept customer orders
* Retrieve support documents
* Retrieve order status updates.
Which type of NLP should you use?
Answer: C
Explanation:
The chatbot must interpret user intent and route actions such as accepting orders, retrieving support documents, and retrieving order status. This is language understanding , because the system must understand what the user wants to do from natural language input.
NEW QUESTION # 111
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