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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Features of generative AI workloads on Azure20–25%- Describe generative AI concepts
- Describe capabilities of Azure OpenAI Service
- Describe use cases for generative AI
- Describe responsible AI practices for generative AI
Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Translator
- Describe capabilities of Azure Language
- Describe capabilities of Azure Speech
- Identify types of NLP solutions
Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Form Recognizer
- Describe capabilities of Azure Computer Vision
- Describe capabilities of Azure Face
- Identify types of computer vision solutions
- Describe capabilities of Azure Custom Vision
Fundamental principles of machine learning on Azure15–20%- Describe core concepts of machine learning
- Describe machine learning pipelines
- Describe capabilities of Azure Machine Learning
- Describe automated machine learning
Artificial Intelligence workloads and considerations15–20%- Identify types of AI workloads
- Describe considerations for developing AI solutions
- Describe responsible AI principles

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Microsoft Azure AI Fundamentals Sample Questions (Q97-Q102):

NEW QUESTION # 97
Which Computer Vision feature can you use to generate automatic captions for digital photographs?

Answer: A

Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of computer vision workloads on Azure," the "Describe Image" feature within Azure's Computer Vision service automatically generates textual captions that describe the contents of an image.
This feature uses a deep learning model trained on millions of labeled images to identify objects, people, and scenes, then formulates a natural language sentence summarizing what it sees. For example, given a photograph showing "two people sitting at a table with a laptop," the service might generate the caption: "A man and a woman sitting at a desk using a laptop." Here's how the other options differ:
* A. Recognize text: Refers to Optical Character Recognition (OCR), which extracts written text from images, not generates descriptive captions.
* C. Identify the areas of interest: Refers to detecting regions of an image that stand out visually, such as hotspots or significant features, not descriptive captioning.
* D. Detect objects: Identifies and classifies objects in an image (e.g., cars, chairs, people) but doesn't produce a sentence or caption summarizing them.
Thus, only "Describe the images" generates automatic, human-readable captions that summarize photo content, a core computer vision workload taught in AI-900.


NEW QUESTION # 98
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:


NEW QUESTION # 99
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 100
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn documentation on GitHub Copilot, the GitHub Copilot extension for Microsoft Visual Studio Code (VS Code) is powered by the OpenAI Codex model, a specialized descendant of the GPT (Generative Pre-trained Transformer) family of models. The OpenAI Codex model is specifically fine-tuned for programming tasks- it understands and generates code in multiple programming languages such as Python, JavaScript, C#, and more.
GitHub Copilot functions as an AI-powered coding assistant that suggests complete lines or blocks of code, helps write comments, and can even generate functions based on natural language prompts entered by the developer. When a user types a comment like "// sort a list of numbers in ascending order," Copilot uses the Codex model to understand the intent and generate an appropriate code implementation.
The integration with Visual Studio Code allows developers to work efficiently without needing to switch between documentation and their coding environment. GitHub Copilot leverages context-aware suggestions, meaning it understands the surrounding code, function definitions, and variable names to provide relevant completions.
The other options are incorrect because:
* GitHub source control manages code repositories but doesn't use AI models.
* IntelliSense is a built-in VS Code feature for code completion but doesn't rely on OpenAI models.
* Microsoft 365 Copilot uses large language models (like GPT-4) for productivity tools such as Word, Excel, and Outlook-not for code generation.
Therefore, the correct choice that uses the OpenAI Codex model in Visual Studio Code is GitHub Copilot, aligning with Microsoft's AI-900 learning objectives under "Identify common use cases for Azure OpenAI and GitHub Copilot."


NEW QUESTION # 101
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Explanation

With Microsoft's Conversational AI tools developers can build, connect, deploy, and manage intelligent bots that naturally interact with their users on a website, app, Cortana, Microsoft Teams, Skype, Facebook Messenger, Slack, and more.
Reference:
https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-to-build-connect-and


NEW QUESTION # 102
......

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