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| Section | Weight | Objectives |
|---|---|---|
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language |
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe core concepts of machine learning - Describe machine learning pipelines |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
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NEW QUESTION # 173
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
NEW QUESTION # 174
You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.
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 # 175
In which two scenarios can you use the Azure Al Document Intelligence service (formerly Form Recognizer)?
Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
The Azure AI Document Intelligence service (formerly Form Recognizer) is designed to analyze, extract, and structure data from scanned or digital documents such as invoices, receipts, contracts, and forms. According to the Microsoft Learn module "Extract data from documents with Azure AI Document Intelligence", the service uses optical character recognition (OCR) and pretrained machine learning models to automatically extract key information.
* A. Extract the invoice number from an invoice - YESThe prebuilt invoice model in Document Intelligence can detect and extract key fields such as invoice number, date, total amount, tax, and vendor details from scanned or digital invoices.
* B. Identify the retailer from a receipt - YESThe prebuilt receipt model can recognize fields like merchant name (retailer), transaction date, total spent, and tax amount, making this option correct as well.
* C. Find images of products in a catalog - NOThis is a computer vision or Custom Vision use case, not a document data extraction task.
* D. Translate a form from French to English - NOTranslation involves Azure AI Translator, part of the Language service, not Document Intelligence.
Hence, the correct and Microsoft-verified answers are:
# A. Extract the invoice number from an invoice
# B. Identify the retailer from a receipt
NEW QUESTION # 176
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
Graphical user interface, text, application Description automatically generated
Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
NEW QUESTION # 177
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Table Description automatically generated with medium confidence
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
NEW QUESTION # 178
......
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