Microsoft AI-901 Korean Value Pack (Frequently Bought Together)

Correct Answer:

Explanation:

Evaluating model outcomes across demographic groups to reduce bias is an example of the Microsoft responsible AI principle of fairness.
The Microsoft responsible AI principle of fairness means AI systems should treat all people fairly and avoid unfair bias or discrimination across groups.
The other options are incorrect:
accountability means people and organizations remain responsible for AI systems and their effects.
privacy and security focuses on protecting data and controlling access.
transparency means users should understand when and how AI is being used, including system capabilities and limitations.

Correct Answer:

Explanation:

Azure Content Understanding in Foundry Tools uses transcription to convert speech to text from audio and video.
Transcription is the process of converting spoken audio into written text. In Azure Content Understanding, audio and video analyzers can use transcription to produce text transcripts from spoken content.
The other options are incorrect:
diarization identifies or separates speakers, such as "Speaker 1" and "Speaker 2." key phrase extraction identifies important phrases from text after text exists.
synthesis converts text into spoken audio, which is the opposite direction of speech-to-text.

Correct Answer: B,E  🗳️

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Correct Answer:

Explanation:

Statement 1: A summarization model is used to transcribe spoken audio into written text. = No Summarization creates a shorter version of existing text. Transcribing spoken audio into written text is a speech recognition / speech-to-text capability, not summarization.
Statement 2: A chat completion model can generate vector embeddings for semantic tasks. = No Chat completion models are used to generate conversational text responses. Vector embeddings for semantic search, similarity, and retrieval tasks are generated by embedding models, not chat completion models.
Statement 3: An embedding model can generate vector representations of the provided input. = Yes This is correct. Embedding models convert input such as text into vector representations that capture semantic meaning and can be used for tasks such as similarity search, clustering, and retrieval.

Correct Answer: A  🗳️

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