Dumps AI-901 Questions & AI-901 Exam Objectives

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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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Free PDF Quiz AI-901 - Microsoft Azure AI Fundamentals Updated Dumps Questions

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Microsoft Azure AI Fundamentals Sample Questions (Q101-Q106):

NEW QUESTION # 101
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: Fairness can be achieved by focusing solely on improving the overall accuracy of an AI model. = No Overall accuracy alone does not guarantee fairness. A model can have high overall accuracy while still producing biased or lower-quality outcomes for specific demographic groups. Fairness requires evaluating and reducing differences in model behavior across groups.
Statement 2: Evaluating AI system outputs to identify and reduce bias across demographic groups supports fairness. = Yes This statement is correct. The Microsoft responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias. Evaluating outputs across demographic groups helps identify and reduce bias.
Statement 3: Ensuring fairness for an AI system means that all users always receive the same output from the system. = No Fairness does not mean identical outputs for all users. It means the system should avoid unfair bias and provide equitable treatment. Different users may receive different outputs when differences are relevant and appropriate.


NEW QUESTION # 102
Select the answer that correctly complete the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
Information extraction solutions that detect and read text in scanned documents and images rely on computer vision .
Detecting and reading text in scanned documents and images is typically done with OCR , which is a computer vision capability. Microsoft describes OCR as text recognition/text extraction that extracts printed or handwritten text from images and documents.


NEW QUESTION # 103
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, an agent must be published as an Agent Application before external production applications can send requests to it through a stable, production-ready endpoint.
While you can write code, build, and test an agent inside a shared development workspace (such as an Azure AI Foundry project), it is not designed for broad external distribution in that state.
Box 2: No
No, you do not need to fine-tune a model before you can publish an AI agent in the Microsoft Foundry portal.
When building an agentic workflow in Microsoft Foundry Agent Service, you can attach a standard, out-of-the-box foundation model deployment (such as GPT-4o or gpt-4o-mini) to your agent definition. Fine-tuning is entirely optional and is reserved for specific performance optimization.
Box 3: No
In the Microsoft Foundry portal, an agent is not published automatically when you save its configuration; saving only updates your draft, and you must explicitly select the publish option to make it live.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/agent-applications
https://itnext.io/how-microsoft-foundry-makes-ai-agents-unstoppable-with-fine-tuning-and-rft-5abeeb55f662


NEW QUESTION # 104
You are building a Conversational Language Understanding model for an e-commerce business.
You need to ensure that the model detects when utterances are outside the intended scope of the model.
What should you do?

Answer: B

Explanation:
The None intent is a required intent and can't be deleted or renamed. The intent is meant to categorize utterances that don't belong to any of your other custom intents. An utterance can be predicted as the None intent if the top scoring intent's score is lower than the None score threshold.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language- understanding/concepts/none-intent


NEW QUESTION # 105
What can be used to analyze scanned invoices and extract data, such as billing addresses and the total amount due?

Answer: C

Explanation:
The Document Intelligence invoice model uses powerful Optical Character Recognition (OCR) capabilities to analyze and extract key fields and line items from sales invoices, utility bills, and purchase orders.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/prebuilt/invoice


NEW QUESTION # 106
......

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AI-901 Exam Objectives: https://www.itdumpsfree.com/AI-901-exam-passed.html

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