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Microsoft AI-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement AI solutions by using Microsoft Foundry55-60%- Work with models in Microsoft Foundry
  • 1. Evaluate model performance and safety
  • 2. Discover and deploy foundation models
  • 3. Fine-tune models
  • 4. Customize models with prompt engineering
- Implement AI solutions
  • 1. Integrate AI services into applications
  • 2. Monitor and manage AI solutions
  • 3. Implement single-agent solutions
  • 4. Use code examples to call AI services
- Get started with Microsoft Foundry
  • 1. Create and configure a Foundry project
  • 2. Describe core features and use cases of Microsoft Foundry
  • 3. Navigate the Microsoft Foundry portal
Topic 2: Identify AI concepts and responsibilities40-45%- Identify AI concepts and features
  • 1. Describe capabilities of natural language processing
  • 2. Describe types of AI workloads
  • 3. Describe characteristics of generative AI
  • 4. Describe capabilities of computer vision
  • 5. Describe capabilities of machine learning
- Describe principles of responsible AI
  • 1. Describe accountability considerations
  • 2. Describe reliability and safety considerations
  • 3. Describe inclusiveness considerations
  • 4. Describe fairness considerations
  • 5. Describe privacy and security considerations
  • 6. Describe transparency considerations
- Identify AI model components and configurations
  • 1. Describe model deployment and consumption concepts
  • 2. Describe model training and evaluation concepts
  • 3. Describe data preparation for AI models

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Microsoft Azure AI Fundamentals Sample Questions (Q33-Q38):

NEW QUESTION # 33
Capturing text from images is an example of which type of AI capability?

Answer: D

Explanation:
Capturing text from images is the exact definition of optical character recognition (OCR).
How OCR Works
Image input: Software takes a photo, scan, or graphic containing letters.
Shape analysis: It scans the pixels to find light and dark areas that match letters or numbers.
Text output: It changes the visual shapes into real, editable digital text.
Reference:
https://en.wikipedia.org/wiki/Optical_character_recognition


NEW QUESTION # 34
You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following table.

Which type of natural language processing was performed?

Answer: C

Explanation:
The output identifies text values and labels them with entity categories, such as:
now [DateTime]
students [PersonType]
Microsoft [Organization]
175 [Quantity-Number]
Today [DateTime-Date]
This is entity recognition, also known as Named Entity Recognition (NER). NER identifies and categorizes entities in unstructured text, such as people, organizations, locations, dates, quantities, numbers, and other named or structured values.
Why the other options are incorrect:
B). translation would convert text from one language to another.
C). key phrase extraction would return important phrases or main topics, not categorized entity labels.
D). sentiment analysis would return sentiment such as positive, negative, neutral, or mixed.


NEW QUESTION # 35
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:
The correct answer is Creating captions for a video recording . Speech recognition, also called speech-to-text , converts spoken audio into textual output. Microsoft explicitly defines Azure Speech-to-Text as speech recognition technology that transcribes audio streams or prerecorded audio into text.
Caption generation is a direct application of this capability. Microsoft describes captioning as converting the audio content of a video, film, webcast, or other production into text and displaying that text visually.
Therefore, creating captions from the spoken content of a video is a canonical speech-recognition workload.
Creating an audio commentary is instead associated with speech synthesis or text-to-speech because the required output is spoken audio. Identifying key phrases in a video transcript is a text-analysis/NLP task performed after the speech has already been transcribed. A voice-activated security system may involve voice authentication, speaker recognition, or command recognition, but it is not as unambiguously a speech-to-text workload as caption generation.
The AI-901 Study Guide specifically requires candidates to distinguish the features and capabilities of speech recognition and speech synthesis .


NEW QUESTION # 36
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:


NEW QUESTION # 37
You are creating an app to help employees write emails and reports based on user prompts.
What should you use?

Answer: C

Explanation:
Azure OpenAI Service can generate text based on user prompts. It leverages GPT (Generative Pre-trained Transformer) models that are trained to understand and generate human-like text.
Users interact with these models by providing a prompt, and the model attempts to produce the most likely continuation or completion of that text.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models


NEW QUESTION # 38
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