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Microsoft Azure AI Fundamentals Sample Questions (Q54-Q59):

NEW QUESTION # 54
You plan to develop an AI application that will read the license plates of motor vehicles by using Azure AI Foundry. What should you use to develop the application?

A. Copilot for Microsoft 365
B. Azure AI Studio
C. GitHub Actions
D. Microsoft Visual Studio Code

Answer: B

Explanation:
Azure AI Studio (now Azure AI Foundry) is used to develop an AI application for reading license plates using its tools and models. The platform allows developers to build, customize, and deploy AI agents and applications, including those that process images to extract information like license plate numbers.
Key components of the process
Platform: Azure AI Foundry (formerly Azure AI Studio) is the central platform for building and managing AI applications.
Tools: You can use its integrated development environment (IDE), SDKs, and APIs to develop and customize models.
Models: The platform provides access to a wide range of AI models, including computer vision models capable of optical character recognition (OCR), which can be used to read text from images.
Workflow:
Create an Azure AI Foundry project in Azure.
Use the model catalog to find and deploy a suitable computer vision or OCR model.
Integrate the model into your application using the SDK or API.
For a license plate application, you would feed images to the model, and the model would output the recognized text from the license plate.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry

NEW QUESTION # 55
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 # 56
You plan to create an AI application that will use Azure OpenAI Service. The solution requires that a specific amount of throughput be allocated. Which type of deployment should you use?

A. standard
B. data zone standard
C. provisioned
D. global batch

Answer: C

Explanation:
When a specific amount of throughput is needed with Azure OpenAI Service, you should use Provisioned Throughput Units (PTUs) for your deployment. PTUs allow you to reserve a fixed amount of processing capacity for your models, ensuring consistent performance and avoiding potential throttling or rate limits.
In contrast, the "Standard" deployment option in Azure OpenAI Service uses a "pay-as-you-go" model, where you are billed for the amount of usage. While this can be suitable for applications with fluctuating usage patterns, it does not provide the same level of throughput control as PTUs.
In summary: When you need to allocate a specific amount of throughput with Azure OpenAI Service, choose the "Provisioned" deployment type, which allows you to use PTUs.
Reference:
https://github.com/MicrosoftDocs/azure-ai-docs/blob/main/articles/ai-
services/openai/concepts/provisioned-throughput.md

NEW QUESTION # 57
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?

A. Compliance
B. Tools
C. Model leaderboard
D. Evaluator catalog

Answer: C

Explanation:
To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.
Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.
Why the other options are incorrect:
A . Evaluator catalog is for selecting evaluators to measure model or application outputs, not comparing LLM costs.
C . Compliance relates to governance and compliance, not model cost comparison.
D . Tools provides Foundry tools, not benchmarked cost comparison across models.

NEW QUESTION # 58
Capturing text from images is an example of which type of AI capability?

A. optical character recognition (OCR)
B. text analysis
C. image description
D. object detection

Answer: C

Explanation:
Optical Character Recognition (OCR) is a technology that converts images of text (like scanned documents or photos) into machine-readable, editable text. It's used to extract text from various sources and make it accessible for digital editing, searching, and other manipulations.
Reference:
https://www.ibm.com/think/topics/optical-character-recognition

NEW QUESTION # 59
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Microsoft Azure AI Fundamentals Sample Questions (Q54-Q59):

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