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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 (Q54-Q59):

NEW QUESTION # 54
You deploy an Al system to assist with hiring decisions.
Company policy requires that human reviewers oversee Al-generated recommendations and remain responsible for final hiring decisions.
Which Microsoft responsible Al principle is this an example of?

Answer: B


NEW QUESTION # 55
You have an Al based loan approval system During testing, you discover that the system has a gender bias.
Which responsible Al principle does this violate?

Answer: D

Explanation:
A loan approval system that shows gender bias violates the Microsoft responsible AI principle of fairness .
Fairness means AI systems should avoid unfair bias and should treat people equitably across demographic groups such as gender, race, age, and other protected or sensitive characteristics.


NEW QUESTION # 56
You plan to create an Al application that will use Azure OpenAI in Foundry Models. The solution requires that a specific amount of throughput be allocated.
Which type of deployment should you use?

Answer: C


NEW QUESTION # 57
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text.
Named Entity Recognition identifies and categorizes entities in text, such as people, organizations, locations, dates, times, quantities, currencies, and other structured information.
The other options are incorrect:
Key phrase extraction identifies important phrases or main topics.
Language detection identifies the language of the text.
Sentiment Analysis identifies positive, negative, neutral, or mixed sentiment.


NEW QUESTION # 58
You are developing an application that will coordinate a travel-booking workflow by gathering user preferences, searching for options, requesting approval, and booking services. The solution will maintain context across the steps.
Which type of Al workload is this an example of?

Answer: B

Explanation:
The described solution coordinates a multi-step workflow: gathering user preferences, searching for options, requesting approval, booking services, and maintaining context across steps. This is an example of Agentic AI.
Agentic AI systems use AI agents to reason over user goals, maintain context, decide which actions to take, and use tools or services to complete tasks across multiple steps.
Why the other options are incorrect:
A). information extraction extracts structured data from content.
C). computer vision analyzes images or video.
D). generative AI creates new content, but the scenario specifically describes an autonomous, context-aware workflow with actions, which is Agentic AI.


NEW QUESTION # 59
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