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Microsoft AI-901 Exam Syllabus Topics:
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Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901
Microsoft Azure AI Fundamentals Sample Questions:
You are developing an AI-powered customer support application.
Which task is an example of the Microsoft responsible AI principle of inclusiveness?
- A. Design the interface to support multiple languages and screen readers.
- B. Provide explanations about how predictions are generated.
- C. Evaluate model outputs across demographic groups to reduce bias.
- D. Encrypt stored customer data and restrict access by using role-based controls.
Correct Answer: A 🗳️
Explanation: Only visible for TestBraindump members. You can sign-up / login (it's free).
Select the answer that correctly completes the sentence.
Correct Answer:

Explanation:
The Microsoft responsible AI principle of transparency requires that AI systems be explainable to users.
Transparency means users should understand when they are interacting with AI, what the system can and cannot do, and how outputs or predictions are generated at an appropriate level.
The other options map to different responsible AI principles:
protect sensitive user data = Privacy and security
reduce bias in decisions = Fairness
require human oversight = Accountability
Select the answer that correctly completes the sentence.
Correct Answer:

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.
You are developing a system to predict the prices of insurance for drivers in the United Kingdom. You need to minimize unintended bias in the system. What should you do?
- A. Take a training sample that is representative of the population in the United Kingdom.
- B. Take a completely random training sample.
- C. Remove information about protected characteristics from the data before sampling.
- D. Create a training dataset that uses data from global insurers
Correct Answer: A 🗳️
Explanation: Only visible for TestBraindump members. You can sign-up / login (it's free).
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.
Correct Answer:

Explanation:
AudioOutputConfig(filename= " output.wav " )
The question specifically states the application must save the synthesized audio to a file. In the Azure Speech SDK for Python, speechsdk.audio.AudioOutputConfig(filename= " output.wav " ) directs the synthesizer to write the generated speech output directly to a WAV file on disk - which is exactly the requirement.
Why the other options are wrong:
AudioOutputConfig(stream) - This routes audio output to an in-memory audio stream object, not a file. It is used when you want to process or play the audio programmatically without saving it to disk.
AudioStreamFormat(wave_stream_format=AudioStreamWaveFormat.PCM) - This class defines the format of an audio stream (e.g., PCM encoding, sample rate). It is used when configuring custom audio streams, not when specifying a file output destination. It is not a valid argument for speechsdk.audio. in this context.
The correct and complete line is:
audio_config = speechsdk.audio.AudioOutputConfig(filename= " output.wav " )
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