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NEW QUESTION # 24
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?
Answer: D
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 # 25
You have an Azure subscription.
You need to use Azure Content Understanding in Foundry Tools to extract structured data from invoices.
What should you provision?
Answer: C
Explanation:
To use Azure Content Understanding in Foundry Tools, Microsoft lists a Microsoft Foundry resource as a prerequisite. The documentation states that you need a Microsoft Foundry resource created in a supported region, and that the portal lists this resource under Foundry > Foundry.
The invoice scenario is also directly aligned with Content Understanding's intelligent document processing use case: Microsoft states that Content Understanding converts unstructured documents into structured data and gives invoice processing as an example.
Therefore, to extract structured data from invoices by using Azure Content Understanding in Foundry Tools, you should provision a Microsoft Foundry resource.
NEW QUESTION # 26
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 # 27
You are developing an application that analyze invoices by using Azure Content Understanding in Foundry Tools.
You need to ensure that the application retrieves the analysis results after processing completes.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The completed code is:
poller = client.begin_analyze(
analyzer_id= " invoice " ,
input_url=url
)
result = poller.result()
Azure Content Understanding analysis uses a long-running operation pattern. The Python SDK returns a poller from begin_analyze(), and Microsoft documentation states that the SDK poller handles polling automatically when you call .result().
Therefore, to retrieve the analysis results after processing completes, the correct option is:
result
The other options are incorrect because status checks operation state, wait waits without returning the final analysis object, and get_results is not the method shown for retrieving the begin_analyze() result in this code pattern.
NEW QUESTION # 28
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Azure Content Understanding in Foundry Tools uses transcription to convert speech to text from audio and video.
Transcription is the process of converting spoken audio into written text. In Azure Content Understanding, audio and video analyzers can use transcription to produce text transcripts from spoken content.
The other options are incorrect:
diarization identifies or separates speakers, such as "Speaker 1" and "Speaker 2." key phrase extraction identifies important phrases from text after text exists.
synthesis converts text into spoken audio, which is the opposite direction of speech-to-text.
NEW QUESTION # 29
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