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Microsoft AI-901 Exam Prep
Microsoft Azure AI Fundamentals (Page 3 )

Updated On: 12-Oct-2026
Page 3 of 27
PDF with 158 Questions

HOTSPOT (Drag and Drop is not supported)
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box 1: No No - Voice Live returns only transcribed text.
Azure AI's Voice Live API does not return only transcribed text. It is a fully managed, end-to-end, low-latency speech-to-speech pipeline.Instead of forcing you to build separate steps for speech-to-text, LLM processing, and text-to-speech, the Azure Voice Live API combines these into a single WebSocket connection.
Box 2: No No - Voice Live requires you to separately implement speech to text and text to speech services.
You do not need to implement them separately. The Azure Voice Live API is a fully managed service that integrates speech-to-text, generative AI, and text-to-speech into a single unified pipeline. It handles both listening and speaking through one WebSocket interface, eliminating complex patchwork code.
Box 3: Yes Voice Live combines speech to text, reasoning, and text to speech into a single conversational experience.
The Azure Voice Live API is a fully managed service that integrates speech-to-text, generative AI, and text-to-speech into a single unified pipeline.


Reference:

https://learn.microsoft.com/en-us/azure/ai-services/speech-service/voice-live-language-support



HOTSPOT (Drag and Drop is not supported)
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.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box: recognizer.recognize_once() The recognizer.recognize_once() method belongs to the SpeechRecognizer object. Its purpose is to start speech recognition from your audio input (like a microphone) and stop after a single utterance is parsed into text. This perfectly fulfills the goal of creating an application that "listens for spoken commands and converts them into text".
Purpose: The recognizer.recognize_once() or recognizer.recognize_once_async() method instructs the Azure Speech Recognizer to start listening to the audio input stream.
Behavior: It captures a single spoken utterance (like a short command), performs speech recognition, and returns the recognized text.


Reference:

https://gotranscript.com/public/developing-speech-to-text-with-c-sharp-sdk



What are two purposes of instructions when prompting a generative AI model? Each correct answer presents part of the solution.
Note: Each correct selection is worth one point.

  1. defines the agent’s role and behavior
  2. selects which model to use
  3. defines the Azure region where inference occurs
  4. defines the tokens per minute (TPM) allocation for the model
  5. defines constraints on the model’s responses

Answer(s): A,E

Explanation:

Giving a generative AI model clear, structural instructions (commonly referred to as Prompt Engineering) is the standard method used to define its persona, guide its behavior, and place strict operational boundaries on its responses.This is typically achieved through explicit instruction sets like System Instructions and targeted Prompt Guidelines.
1. Defining the Agent's Role and BehaviorEstablishing a persona prevents the AI from wandering into areas outside its intended scope and ensures the delivery style matches your needs.
2. Defining Constraints on ResponsesConstraints act as guardrails that prevent the AI from hallucinating, rambling, or providing incorrect information.


Reference:

https://ai.google.dev/gemini-api/docs/prompting-strategies



HOTSPOT (Drag and Drop is not supported)
You are developing an application that converts text into spoken audio and saves the synthesized audio to a file 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.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box: AudioOutputConfig(filename="output.wav")
To complete the code and save the synthesized audio directly to a file, use the code AudioOutputConfig (filename="path_to_file.wav").
Class Used: The AudioOutputConfig class is explicitly used to manage speech synthesis destination routing (such as physical files, speakers, or digital streams).
File Format: The Azure Speech SDK natively saves output audio files generated through this configuration in the WAV format.


Reference:

https://stackoverflow.com/questions/77923835/how-to-save-a-stream-object-in-azure-text-to-speech-without-speaking-the-text-us



HOTSPOT (Drag and Drop is not supported)
Select the answer that correctly completes the sentence.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box: system prompt To define an agent’s role and behaviors, you must configure a ________ for the agent.
Configuring a system prompt (or system message / instructions) is the primary way to define an agent's role, persona, scope, and operational behaviors in Azure AI.System prompts act as the agent's "job description". They dictate how the underlying model interprets intent and executes tasks before it ever sees a user's input.


Reference:

https://learn.microsoft.com/en-us/azure/microsoft-discovery/how-to-prompt-engineering



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