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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.

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Microsoft Azure AI Fundamentals Sample Questions (Q63-Q68):

NEW QUESTION # 63
Hotspot Question
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:
Box 1: No
No - Speech synthesis converts spoked audio into written text.
That statement describes speech recognition (or audio transcription), not speech synthesis.
Speech synthesis does the exact opposite: it converts written text into spoken audio.
Box 2: Yes
Yes - Speech recognition can detect and transcribe spoken input.
Speech recognition (also known as automatic speech recognition or speech-to-text) detects acoustic sound waves from spoken input and converts them into written text or actionable digital commands.
Box 3: No
No - Text to speech in Azure Speech in Foundry Tools can translate text input into audible speech in a different language.
That is not entirely correct because Text-to-Speech (TTS) on its own only converts written text into audible speech within the same language; it does not perform language translation.
To translate text input into audible speech in a different language using Azure Speech in Foundry Tools, you must combine or use specific features within the ecosystem:
How Translation Works in Azure Speech
The Translator Tool: If you start with a text input, you must first pass it through the Azure AI Translator (or a foundational Language Model inside Azure AI Foundry) to translate the text from the source language to the target language.
The Text-to-Speech Feature: Once the text is translated, the Text-to-Speech engine synthesizes that translated text into natural-sounding audio using a voice native to the target language.
Speech Translation Feature: If your original input is spoken audio rather than text, Azure Speech offers a dedicated Speech Translation feature. This combines Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS) into a single workflow to achieve real-time speech-to-speech or speech-to-text translation.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/responsible-ai/speech-service/text-to-speech/transparency-note


NEW QUESTION # 64
Select the answer that correct completes the sentence.

Answer:

Explanation:

Explanation:
Azure Text Analytics client library
The completed sentence is:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure Language text analysis features include sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics capabilities. These are accessed by using the Azure Text Analytics client library .
The highlighted Azure Speech SDK option is incorrect because it is used for speech workloads, such as speech recognition and speech synthesis, not text analysis.


NEW QUESTION # 65
Hotspot Question
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:
Box 1: No
No, an Azure Content Understanding analyzer does not return results in HTML format. Instead, it processes your input files (which can include HTML) and structures the extracted output into Markdown, JSON fields, and segments.
Box 2: No
Only if you use the newly released synchronous operations introduced in the 2026-06-01-preview API version. If you are using the standard or generally available (GA) versions of the API, the process runs asynchronously by default.
Box 3: Yes
Yes, Azure Content Understanding in Foundry Tools can ingest and extract structured information from documents, images, audio, and video files Multimodal Ingestion: Processes unstructured data across text, images, audio recordings, and video files into user-defined formats like JSON or Markdown.
Schema-Driven Extraction: Uses prebuilt or custom schemas to extract, classify, or generate specific fields without complex prompt engineering.
Confidence & Grounding: Returns confidence scores and source grounding to verify accuracy and minimize manual oversight.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/analyzer-reference
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/whats-new


NEW QUESTION # 66
Which feature of the Azure Language in Foundry Tools service should you use to automate the masking of names and phone numbers in text data?

Answer: A

Explanation:
To automate masking of names and phone numbers in text data, use Personally Identifiable Information (PII) detection in Azure Language.
PII detection identifies sensitive personal information such as names, phone numbers, email addresses, addresses, and other personal identifiers. It can be used to detect and redact or mask that information from text.
The other options are incorrect:
A). custom text classification categorizes documents or text into labels.
C). entity linking identifies entities and links them to knowledge base entries.
D). custom named entity recognition (NER) can extract custom entities, but for built-in masking of names and phone numbers, PII detection is the correct feature.


NEW QUESTION # 67
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 # 68
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