You have the following REST API request.Which Azure OpenAI model should you use to process the request?
Answer(s): A
A DALL-E REST API example using parameters like prompt, size, style, and n (number of images) can be used to generate images. Here's how:Example Request Body (JSON):{ "prompt": "A cat wearing a hat, standing on a skateboard", "size": "1024x1024", // Supported values: "1024x1024", "1792x1024", "1024x1792" "style": "natural", // DALL-E 3 supports "natural" and "vivid" styles "n": 1 // DALL-E 3 only supports n=1 }
https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/dall-e
HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
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.
https://www.iamdave.ai/blog/understanding-named-entity-recognition-ner-meaning-methods-and-use-cases/
Which natural language processing feature can be used to identify the main talking points in customer feedback surveys?
Answer(s): D
To identify main talking points in feedback using Azure natural language processing, the Key Phrase Extraction feature within the Azure AI Language service is the most appropriate choice. This feature analyzes text and extracts the most relevant and important phrases, effectively highlighting the core topics or themes present in the feedback.
https://learn.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
Stating the source of the data used to train a model is an example of which responsible AI principle?
Transparency in AI refers to openly sharing information about how an AI system is designed, trained, and operates. Stating the source of the data used to train a model is an example of transparency, as it provides clarity about the origins of the data and helps stakeholders understand the model's development process.
You plan to create an AI application that will use Azure OpenAI in Foundry Models. The solution requires that a specific amount of throughput be allocated. Which type of deployment should you use?
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 PTUsIn 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.
https://github.com/MicrosoftDocs/azure-ai-docs/blob/main/articles/ai-services/openai/concepts/provisioned-throughput.md
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