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>> Valid Study AI-901 Questions <<
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NEW QUESTION # 101
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:
In Microsoft Foundry Agent Service, tool_choice controls whether and how an agent uses tools during a run.
Statement 1: Yes
Setting tool_choice to auto lets the agent/model decide whether to call a tool. This statement is true.
Statement 2: No
Setting tool_choice to none does not mean the model decides whether to call a tool. It means the model will not call a tool. This statement is false.
Statement 3: Yes
Setting tool_choice to required means the agent must call one or more tools during the run. This statement is true.
Microsoft documentation describes the tool_choice values as follows: auto allows the model to decide whether to call tools, none prevents tool calls, and required forces one or more tool calls.
NEW QUESTION # 102
You are authoring a Conversational Language Understanding application to support a music festival.
You want users to be able to ask questions about scheduled shows, such as: "Which act is playing on the main stage?" The question "Which act is playing on the main stage?" is an example of which type of element?
Answer: A
Explanation:
In Conversational Language Understanding (CLU), an utterance is the spoken or typed input provided by a user in a conversation. It represents a discrete element of their communication, which can be a single sentence, a question, a command, or any form of human language.?
It's a natural language phrase such as "book 2 tickets to Seattle next Tuesday". Example utterances are added to train the model and the model predicts on new utterance at runtime.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/glossary
NEW QUESTION # 103
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?
Answer: B
Explanation:
To compare the costs, capabilities, and performance of large language models (LLMs) in the Foundry portal, you should use the Model leaderboard.
Why Use the Model Leaderboard
The leaderboard allows you to:
Compare models side-by-side: Evaluate multiple LLMs simultaneously for trade-offs regarding estimated costs, throughput, safety, and quality.
Make informed decisions: Select the most cost-effective model for specific workloads before committing to deployments, reducing the risk of expensive errors or overspending.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/manage-costs
NEW QUESTION # 104
Which natural language processing feature can be used to identify the main talking points in customer feedback surveys?
Answer: B
Explanation:
To identify main talking points in feedback using Azure natural language processing, the Key Phrase Extraction feature within the Azure AI Language service is the most appropriate choice.
This feature analyzes text and extracts the most relevant and important phrases, effectively highlighting the core topics or themes present in the feedback.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 105
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is Creating captions for a video recording. Speech recognition, also called speech-to-text, converts spoken audio into textual output. Microsoft explicitly defines Azure Speech-to-Text as speech recognition technology that transcribes audio streams or prerecorded audio into text.
Caption generation is a direct application of this capability. Microsoft describes captioning as converting the audio content of a video, film, webcast, or other production into text and displaying that text visually.
Therefore, creating captions from the spoken content of a video is a canonical speech-recognition workload.
Creating an audio commentary is instead associated with speech synthesis or text-to-speech because the required output is spoken audio. Identifying key phrases in a video transcript is a text-analysis/NLP task performed after the speech has already been transcribed. A voice- activated security system may involve voice authentication, speaker recognition, or command recognition, but it is not as unambiguously a speech-to-text workload as caption generation.
The AI-901 Study Guide specifically requires candidates to distinguish the features and capabilities of speech recognition and speech synthesis.
NEW QUESTION # 106
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