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NEW QUESTION # 37
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:
Explanation:
Box 1: Yes
Yes, in enterprise environments like Microsoft Foundry, you can select a supported base model from the model catalog and deploy it directly to create a functional inference endpoint.
Box 2: No
Model deployments are created in the management portal or API first, and the playground is just a tool to test and use an already deployed model.
Box 3: Yes
Yes, after deploying an OpenAI model in an Azure or Microsoft Foundry environment, you can immediately use the built-in interactive playground within the portal to send prompts and review responses.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/foundry-models/how-to/deploy-foundry-models
NEW QUESTION # 38
What is the maximum image size that can be processed by using the prebuilt receipt model in Azure AI Document Intelligence?
Answer: C
Explanation:
Document Intelligence receipt model
Supported file size: less than 50 MB; minimum pixels 50 x 50 px; maximum pixels 10,000 x
10,000 px
For custom extraction model training, the total size of training data is 50 MB for template model and 1 GB for the neural model.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/prebuilt/id-document
NEW QUESTION # 39
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 # 40
You have an AI-based loan approval system.
During testing, you discover that the system has a gender bias.
Which responsible AI principle does this violate?
Answer: C
Explanation:
Fairness is the responsible AI principle that addresses gender bias. It ensures that artificial intelligence systems treat all people fairly and do not create or spread unfair differences between groups based on gender, race, or other traits.
Stops Bias: It keeps systems from treating one group worse than another.
Checks Data: It looks at training data to remove unfair patterns.
Protects Users: It ensures equal outcomes for everyone.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai
NEW QUESTION # 41
You have a Python application that extracts fields from invoices by using Azure Content Understanding in Foundry Tools.
You submit a PDF for analysis.
What should the application do to retrieve the results?
Answer: B
Explanation:
To retrieve the extraction results using the official azure-ai-contentunderstanding Python SDK, the application must call begin_analyze(), and then call poller.result() to retrieve the results.
Because document analysis is an asynchronous operation, the process is split into initiating the request and waiting for the long-running operation to complete.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/use-rest-api
NEW QUESTION # 42
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