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NEW QUESTION # 50
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:
Explanation:
Statement 1: In the new Microsoft Foundry portal, you must fine-tune a model before you can deploy the model. = No Fine-tuning is optional. Microsoft's Foundry model deployment documentation describes deploying Foundry Models directly from the model catalog for inference. It does not require fine-tuning first.
Statement 2: In the new Microsoft Foundry portal, you can test a model from the model catalog only after you deploy the model. = No Microsoft documentation states that some Foundry Tools are available to try via the model catalog without a project, and Foundry playgrounds are used for prototyping and validation before production. Therefore, the statement using "only after you deploy" is too restrictive.
Statement 3: In the new Microsoft Foundry portal, you can deploy a model from the model catalog only after retraining the model. = No Retraining/fine-tuning is not required before deployment. Microsoft states that after you deploy a Foundry Model, you can interact with it in the Foundry Playground and use it from code, and the deployment workflow starts by selecting a model from the model catalog and choosing Deploy.
NEW QUESTION # 51
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 52
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:
Explanation:
Statement 1: An Azure Content Understanding in Foundry Tools analyzer returns results in the HTML format. = No Azure Content Understanding returns analysis results as structured output, typically JSON , not HTML.
Microsoft documentation states that the Content Understanding API returns analysis results in a structured JSON format.
Statement 2: The Content Understanding REST API analyzes content synchronously and returns results in the same response. = No Content Understanding analysis is asynchronous. The quickstart states that the response header includes an Operation-Location field, which is used to retrieve the results of the asynchronous analysis operation.
Statement 3: Azure Content Understanding in Foundry Tools can transform documents, images, audio, and video files into structured output. = Yes Microsoft describes Azure AI Content Understanding as processing documents, images, videos, and audio, and transforming them into user-defined output formats.
NEW QUESTION # 53
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure Language text analysis workloads include capabilities such as sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics features. In application code, these text analysis capabilities are accessed by using the Azure Text Analytics client library .
The other options are incorrect:
Azure AI Projects client library is used for Foundry project and agent-related development.
Azure OpenAI client library is used to call Azure OpenAI model deployments.
Azure Speech SDK is used for speech recognition, speech synthesis, and other speech workloads.
NEW QUESTION # 54
You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?
Answer: C
Explanation:
An analyzer in Azure Content Understanding within Foundry Tools can be used for this scenario.
Prebuilt Invoice Support: It offers prebuilt invoice analyzer templates specifically trained to identify common transactional data out of the box.
Schema-Driven Extraction: Instead of manual prompt engineering or tedious labeling, you can supply a strict JSON schema containing the specific fields you need.
Nested Values & Layouts: It handles hierarchical, nested elements (such as itemized tables, nested line items, or customer details) and maps them cleanly into arrays or structured objects.
Web App Integration: Activating the analyzer creates a highly scalable REST API endpoint. Your invoice-processing web app can query this endpoint to receive clean, machine-readable structured JSON payloads ready for downstream expense calculation.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/overview
NEW QUESTION # 55
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