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NEW QUESTION # 116
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 117
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The completed sentence is:
An AI workload that produces new content based on user input is an example of generative AI.
Generative AI is used to create new content, such as text, images, code, audio, or other outputs, from user prompts or instructions.
The other options are incorrect:
content understanding extracts or interprets information from existing content.
information extraction identifies and extracts structured data from content.
text analysis analyzes existing text for entities, sentiment, key phrases, language, or summaries.
NEW QUESTION # 118
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: the Azure Language MCP server
To enable an AI agent in Microsoft Foundry to utilize Azure Language capabilities for text analysis, you must configure the Azure Language Model Context Protocol (MCP) server.
Microsoft Foundry integrates its ecosystem of deterministic AI services through the standardized Model Context Protocol (MCP). This removes the need to write custom routing logic or stitch together multiple standalone APIs.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview
NEW QUESTION # 119
Hotspot Question
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You need to develop an application that uses the Azure OpenAI client library to send prompts to the model.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box: responses
The correct method call uses the OpenAI Responses API, which utilizes
client.responses.create(). This modern endpoint is designed for text and structured generations, moving away from the older chat.completions.create syntax.
Example code:
import os
from openai import OpenAI
# Initialize the client (automatically uses OPENAI_API_KEY environment variable) client = OpenAI()
# Call the Responses API
response = client.responses.create(
model="gpt-4o",
input="Explain quantum computing in one sentence."
)
Reference:
https://developers.openai.com/api/docs/guides/text
NEW QUESTION # 120
Your company has thousands of recorded customer support calls in multiple languages stored as audio files in Azure Storage.
You need to generate text transcripts of all the recordings.
Which Azure Speech in Foundry Tools capability should you use?
Answer: D
Explanation:
For thousands of recorded support calls stored as audio files in Azure Storage, the correct capability is speech to text batch transcription .
Microsoft states that batch transcription is designed to transcribe a large amount of audio data in storage , including audio files in Azure Blob Storage, and that files can be processed concurrently to reduce turnaround time.
Real-time transcription is for live audio, not large stored batches. Text to speech converts text into audio.
Speech translation translates speech between languages, but the requirement is to generate transcripts.
NEW QUESTION # 121
......
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