Latest Updated Microsoft Latest AI-901 Exam Topics - AI-901 Microsoft Azure AI Fundamentals Reliable Braindumps Ebook

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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.

>> Latest AI-901 Exam Topics <<

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Microsoft Azure AI Fundamentals Sample Questions (Q72-Q77):

NEW QUESTION # 72
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

A schema defines which fields to extract when analyzing content.
In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.
The other options are incorrect:
A keyword list does not define the complete structured output fields.
OCR-only processing extracts text, but it does not define structured fields.
A synchronous API call describes a request pattern, not the extraction schema.


NEW QUESTION # 73
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: Yes
Yes - Whisper models in Azure OpenAI Service are used to convert speech to text.
The Whisper model is a speech to text model from OpenAI that you can use to transcribe or translate audio files. The model is trained on a large dataset of English audio and text. The model is optimized for transcribing audio files that contain speech in English.
Box 2: Yes
Yes - Embedding models in Azure OpenAI Service are used to convert text into numeric vectors.
Azure OpenAI Embeddings are a way to convert text data into numerical representations that capture semantic meaning. This allows you to find similar text content or compare documents based on their ideas, even if the wording differs.
Embeddings: a set of models that can convert text into numerical vector form to facilitate text similarity.
Box 3: No
GPT-4.1 models can understand and generate natural language or code. The most capable and cost effective model in the GPT-4.1 family is GPT-4.1 Turbo, which has been optimized for chat and works well for traditional completions tasks as well. GPT-4.1 Turbo is available for use with the Chat Completions API.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/whisper-overview
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models


NEW QUESTION # 74
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
To define an agent ' s role and behaviors, you must configure a system prompt for the agent.
In Microsoft Foundry / Azure AI agent scenarios, the system prompt , also referred to as system instructions
, defines how the agent should behave, what role it should follow, and what constraints it must observe.
The other options are incorrect:
deployment slot relates to hosting/deployment routing, not agent behavior.
embedding index is used for vector search or retrieval scenarios, not defining the agent's role.
fine tuning job changes or adapts model behavior through training, but it is not the standard configuration used to define an agent's role and behavior at runtime.
Therefore, the correct answer is system prompt .


NEW QUESTION # 75
You are developing an application that analyzes uploaded video files by using Azure Content Understanding in Foundry Tools.
You need to ensure that the application extracts structured information from the videos.
What should the application call?

Answer: D


NEW QUESTION # 76
What is the maximum image size that can be processed by using the prebuilt receipt model in Azure AI Document Intelligence?

Answer: A

Explanation:
Document Intelligence receipt model
Supported file size: less than 50 MB; minimum pixels 50 x 50 px; maximum pixels 10,000 x
10,000 px
For custom extraction model training, the total size of training data is 50 MB for template model and 1 GB for the neural model.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/prebuilt/id-document


NEW QUESTION # 77
......

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