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Microsoft AI-900, also known as Microsoft Azure AI Fundamentals, is a certification exam that aims to test an individual's knowledge of artificial intelligence (AI) concepts and how they can be applied to Microsoft Azure. AI-900 Exam covers a range of topics, including machine learning, natural language processing, computer vision, and cognitive services. Microsoft Azure AI Fundamentals certification is designed for professionals who are looking to understand the basics of AI and how it can be used to solve business problems.
NEW QUESTION # 323
Which Azure service can use the prebuilt receipt model in Azure Document Intelligence in Foundry Tools?
Answer: C
NEW QUESTION # 324
In which two scenarios can you use a speech synthesis solution? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
Azure Text to Speech is a Speech service feature that converts text to lifelike speech.
Reference:
https://azure.microsoft.com/en-in/services/cognitive-services/text-to-speech/
NEW QUESTION # 325
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:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION # 326
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right.
Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Graphical user interface, text, application Description automatically generated
Box 1: Image classification
Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos.
Box 2: Object detection
Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images.
Box 3: Semantic Segmentation
Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region.
Reference:
https://developers.google.com/machine-learning/practica/image-classification
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/
NEW QUESTION # 327
You need to generate images based on user prompts. Which Azure OpenAI model should you use?
Answer: A
Explanation:
According to the Microsoft Azure OpenAI Service documentation and AI-900 official study materials, the DALL-E model is specifically designed to generate and edit images from natural language prompts. When a user provides a descriptive text input such as "a futuristic city skyline at sunset", DALL-E interprets the textual prompt and produces an image that visually represents the content described. This functionality is known as text-to-image generation and is one of the creative AI capabilities supported by Azure OpenAI.
DALL-E belongs to the family of generative models that can create new visual content, expand existing images, or apply transformations to images based on textual instructions. Within Azure OpenAI, the DALL-E API enables developers to integrate image creation directly into applications-useful for design assistance, marketing content generation, or visualization tools. The model learns from vast datasets of text-image pairs and is optimized to ensure alignment, diversity, and accuracy in the produced visuals.
By contrast, the other options serve different purposes:
* A. GPT-4 is a large language model for text-based generation, reasoning, and conversation, not for creating images.
* C. GPT-3 is an earlier text generation model, primarily used for language tasks like summarization, classification, and question answering.
* D. Whisper is an automatic speech recognition (ASR) model used to convert spoken language into written text; it has no image-generation capability.
Therefore, when the requirement is to generate images based on user prompts, the only Azure OpenAI model that fulfills this purpose is DALL-E. This aligns directly with the AI-900 learning objective covering Azure OpenAI generative capabilities for text, code, and image creation.
NEW QUESTION # 328
......
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