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NEW QUESTION # 75
Hotspot Question
You are developing a voice application that listens for spoken commands and converts them into text by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 76
You are using the Azure Speech SDK to develop a Python application that supports real-time spoken conversations. Which Azure Speech class should you use to configure the connection to the Azure Speech service?
Answer: C
Explanation:
To configure the connection to the Azure Speech service, you should use the azure.cognitiveservices.speech.SpeechConfig class.
Implementation Details
Initialization: This class holds authentication and endpoint details, such as your Azure resource key and region.
Usage: You pass this configuration object into your real-time processing classes, like SpeechRecognizer (for speech-to-text) or SpeechSynthesizer (for text-to-speech).
Reference:
https://docs.azure.cn/en-us/ai-services/speech-service/how-to-recognize-speech
NEW QUESTION # 77
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text.
Named Entity Recognition identifies and categorizes entities in text, such as people, organizations, locations, dates, times, quantities, currencies, and other structured information.
The other options are incorrect:
Key phrase extraction identifies important phrases or main topics.
Language detection identifies the language of the text.
Sentiment Analysis identifies positive, negative, neutral, or mixed sentiment.
NEW QUESTION # 78
You have a Microsoft Foundry project that contains an agent named Agent1.
You need to ensure that Agent1 always calls an Azure function when the agent responds to user input.
To what should you set tool_choice for Agent1?
Answer: B
Explanation:
Microsoft's Foundry Agent Service documentation states that tool_choice provides deterministic control over tool calling:
auto means the model decides whether to call tools.
required means the model must call one or more tools.
none means the model does not call tools.
Therefore:
A . auto = Incorrect, because the model may or may not call the Azure function.
B . none = Incorrect, because this prevents tool/function calls.
C . required = Correct, because it forces the agent to call a tool.
The Azure OpenAI function-calling documentation also confirms that tool_choice="auto" lets the model decide whether to call a function, while tool_choice="none" forces a user-facing response without a tool call.
NEW QUESTION # 79
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:
Explanation:
Statement 1: The Temperature parameter can be set before deploying a model. = No temperature is an inference/request parameter used when calling or testing a deployed model. It controls randomness in generated responses. It is not a required setting for deploying the model itself.
Statement 2: During inference, the model name is used to route requests to a specific deployment. = No In Azure OpenAI / Microsoft Foundry deployments, application requests are routed to a specific deployment name , even when the SDK parameter is called model. The underlying model name, such as gpt-4.1-mini, is not what routes the request to the deployment.
Statement 3: After a model is deployed, both code and testing tools can be used to interact with the model. = Yes After deployment, you can test the model in Foundry playground/testing tools or call the deployment from application code by using the endpoint, deployment name, and authentication credentials.
NEW QUESTION # 80
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