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NEW QUESTION # 81
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You use the Azure OpenAI Responses API to send a prompt to the model.
You need to provide an image for analysis.
Which content item should you include in the request?
Answer: C
Explanation:
When sending an image for analysis using the Azure OpenAI Responses API within a Microsoft Foundry project, you must include a content item object with the type input_image in the message payload.
Required Content Object Structure
Unlike the standard Chat Completions API (which uses type: "image_url"), the Responses API explicitly structures a multimodal image input as a separate block with the following properties:
type: Must be explicitly set to "input_image".
image_url: A dictionary or a direct parameter containing the target location of the image.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/responses
NEW QUESTION # 82
You have a Python application that extracts fields from invoices by using Azure Content Understanding in Foundry Tools.
You submit a PDF for analysis.
What should the application do to retrieve the results?
Answer: B
Explanation:
To retrieve the extraction results using the official azure-ai-contentunderstanding Python SDK, the application must call begin_analyze(), and then call poller.result() to retrieve the results.
Because document analysis is an asynchronous operation, the process is split into initiating the request and waiting for the long-running operation to complete.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/use-rest-api
NEW QUESTION # 83
You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?
Answer: B
Explanation:
The requirement is to extract structured fields, including nested values, from invoices by using a defined schema. In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.
Microsoft's Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms. Microsoft's quickstart also shows invoice processing with the prebuilt-invoice analyzer to extract structured data from an invoice document.
Why the other options are incorrect:
A . transcription workflow in Azure Speech is for converting audio to text, not invoice field extraction.
B . OCR-only document processing can extract text but does not meet the requirement for structured fields and nested values by schema.
D . Azure AI Search is for indexing and querying content, not defining invoice extraction schemas.
Therefore, the correct answer is C. an analyzer in Azure Content Understanding in Foundry Tools.
NEW QUESTION # 84
You need to create an AI agent in Microsoft Foundry that follows a specific role and behavior when responding to users.
What should you configure?
Answer: D
Explanation:
To create an AI agent that follows a specific role and behavior, you configure system instructions . Microsoft Foundry Agent Service documentation states that agent instructions define goals, constraints, and behavior
.
Option A. tokens per minute (TPM) controls throughput quota, not behavior.
Option C. temperature controls response randomness/creativity, not the agent's role.
Option D. max completion tokens controls response length, not the agent's role or behavioral rules.
Therefore, the correct answer is B. system instructions .
NEW QUESTION # 85
In Azure Machine Learning, what are two ensemble methods for combining models in automated machine learning (automated ML)? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
In Azure Machine Learning's automated machine learning (AutoML), two prominent ensemble methods for combining models are Voting and Stacking.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml 1
NEW QUESTION # 86
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