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| Section | Weight | Objectives |
|---|---|---|
| Describe features of generative AI workloads on Azure | 10-15% | - Azure OpenAI and generative services
|
| Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Text analysis
|
| Describe fundamental principles of machine learning on Azure | 25-30% | - Azure Machine Learning basics
|
| Describe features of computer vision workloads on Azure | 15-20% | - Azure AI Vision services
|
| Describe Artificial Intelligence workloads and considerations | 20-25% | - Responsible AI principles
|
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NEW QUESTION # 34
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 extract structured data from invoices using Azure Content Understanding in Foundry Tools, you must provision a Microsoft Foundry resource (often referred to as an Azure AI services resource configured within a supported region).
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/tutorial/create-custom-analyzer
NEW QUESTION # 35
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 36
What is the maximum image size that can be processed by using the prebuilt receipt model in Azure AI Document Intelligence?
Answer: A
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 # 37
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 38
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: natural language processing
The interactive answering of questions entered by a user as part of an application is a direct example of natural language processing (NLP).
How NLP Powers Interactive Q&A
Text interpretation: The system uses linguistic rules and machine learning models to read and interpret the user's typed input.
Intent recognition: It figures out what the user wants or needs by analyzing the meaning behind the words.
Response generation: It formulates and delivers a human-like reply back through the application interface.
Reference:
https://www.gauthmath.com/solution/1800845210023942/The-interactive-answering-of-questions-entered-by-a-user-as-part-of-an-applicati
NEW QUESTION # 39
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