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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.
Topic 2
  • Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.

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Microsoft Azure AI Fundamentals Sample Questions (Q126-Q131):

NEW QUESTION # 126
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 127
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each connect selection is worth one point.

Answer:

Explanation:

Explanation:

Statement 1: Using Azure Speech in Foundry Tools for batch transcription requires live audio input from a microphone. = No Batch transcription is used to process stored audio files, not live microphone input. Live microphone input is used for real-time speech recognition scenarios.
Statement 2: Azure Speech in Foundry Tools can analyze video frames to detect and label objects while transcribing audio. = No Azure Speech is for speech capabilities such as speech recognition, speech synthesis, and speech translation.
Detecting and labeling objects in video frames is a computer vision/video analysis capability, not an Azure Speech capability.
Statement 3: Speech translation in Azure Speech in Foundry Tools can translate spoken audio into text in another language. = Yes Speech translation converts spoken input in one language into translated text in another language.


NEW QUESTION # 128
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:


NEW QUESTION # 129
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: 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.


NEW QUESTION # 130
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:


NEW QUESTION # 131
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