Struggling with Azure AI-102 exam questions on custom Document Intelligence models? Learn why labeling data, uploading to Azure Blob Storage, and using SAS URLs is key to automating invoice processing. Master the steps to pass the Microsoft AI Engineer Associate certification.
Table of Contents
Question
Your organization, Nutex Inc., manages a large volume of invoices, receipts, and purchase orders from different vendors. These documents follow varying formats, and your team spends a considerable amount of time manually extracting data such as invoice numbers, dates, and total amounts.
You have created a custom Document Intelligence model that can extract the necessary information automatically, regardless of the document format.
What should you do first to train the custom Document Intelligence model to extract data from these documents?
A. Label the data, upload it to an Azure Blob Storage container, and provide the storage container’s SAS URL.
B. Define a schema for the custom model directly in the Azure portal.
C. Test the prebuilt model using sample invoices.
D. Use Azure Cognitive Search to preprocess the documents before training.
Answer
A. Label the data, upload it to an Azure Blob Storage container, and provide the storage container’s SAS URL.
Explanation
In the given scenario, the first step would be to label the data, upload it to an Azure Blob Storage container, and provide the storage container’s shared access utility (SAS) URL.
You would follow the below-outlined steps to train the custom Document Intelligence model:
- Label the data and upload it to an Azure Blob Storage container.
- Generate an SAS URL for the container.
- Utilize the Build model Rest API function.
- Utilize the Get model Rest API function to get the trained model ID.
- After training, evaluate or test the model using new, unseen documents to verify accuracy.
- Deploy the trained model to make it available for real-time document analysis.
Sample forms would be stored in Azure Blob storage. An ocr.json file can be generated for each sample form by using the Analyze document function. You can also create a fields.json file to specify the fields that you want to extract and a labels.json file for mapping the fields in the form.
Testing the prebuilt model using sample invoices is not the first step in the given scenario. Testing prebuilt models does not involve training and is not a part of the custom model training process. It is useful for a quick evaluation, but prebuilt models are generic and may not provide the accuracy required for custom formats.
Defining a schema for the custom model directly in the Azure portal is not the first step in the given scenario. Azure AI Document Intelligence does not require you to manually define a schema for training a custom model. In data extraction systems, a schema is used to define the structure of the data being processed. It specifies the fields or entities that need to be extracted and their expected types or formats.
Using Azure Cognitive Search to preprocess the documents before training is not the first step in the given scenario. Azure Cognitive Search is designed for indexing and searching document content and is not involved in the custom training process for Azure AI Document Intelligence.
Microsoft Azure AI Engineer Associate AI-102 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Engineer Associate AI-102 exam and earn Microsoft Azure AI Engineer Associate AI-102 certification.