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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 - Professional Test Microsoft Azure AI Fundamentals Simulator Free

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Microsoft Azure AI Fundamentals Sample Questions (Q121-Q126):

NEW QUESTION # 121
You have a website that includes customer reviews.
You need to store the reviews in English and present the reviews to users in their respective language based on each user ' s geographical location.
Which type of natural language processing workload should you use?

Answer: B


NEW QUESTION # 122
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 123
Capturing text from images is an example of which type of Al capability?

Answer: B


NEW QUESTION # 124
What should you use to compare benchmark metrics of different AI models?

Answer: C

Explanation:
To compare benchmark metrics across Azure AI models, navigate to the Azure AI Foundry portal, explore the model catalog, and use the benchmarking tools to assess model performance. You can then compare these metrics to choose the most suitable model for your needs.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/benchmark-model-in-catalog


NEW QUESTION # 125
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
Box 1: Named Entity Recognition (NER)
Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text, along with other categories like names of people, organizations, and monetary values.
Reference:
https://www.iamdave.ai/blog/understanding-named-entity-recognition-ner-meaning-methods-and-use-cases/


NEW QUESTION # 126
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