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Answer: Collect a set of labeled feedback forms, train a custom model using Azure Custom Document Understanding, and evaluate its performance on a separate set of test forms.
To design and train a custom model for extracting key information from customer feedback forms, you should first collect a set of labeled feedback forms that represent the specific types of information you want to extract. Then, you can use Azure Custom Document Understanding to train a custom model on these forms. After training, you should evaluate the model's performance on a separate set of test forms to ensure it meets your accuracy requirements. Option B is incorrect because using prebuilt models without customization may not capture the specific information required from the feedback forms. Option C is incorrect because manually extracting data and training a custom machine learning model is not the recommended approach for this use case. Option D is incorrect because creating a new Azure AI Search service and configuring it to use a custom skill is not the appropriate approach for training a custom document intelligence model.
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Your company is planning to implement an Azure AI Document Intelligence solution to automate the extraction of key information from customer feedback forms. What are the key steps you should follow to design and train a custom model for this use case?
A
Collect a set of labeled feedback forms, train a custom model using Azure Custom Document Understanding, and evaluate its performance on a separate set of test forms.
B
Use prebuilt models to extract data from the feedback forms without any customization.
C
Manually extract data from a sample of feedback forms and use this data to train a custom machine learning model using Azure Machine Learning.
D
Create a new Azure AI Search service and configure it to use a custom skill for extracting data from the feedback forms.