AI-103 Information Extraction Practice Test 2 — 15 Questions

Question 1

You are developing an application that uses Azure AI Document Intelligence to extract data from receipts. You need to ensure that the extracted data includes the merchant name, transaction date, and total amount. You also want to handle documents where the receipt is rotated or skewed. Which TWO actions should you perform? (Choose two.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Ensure the input documents are in a supported format such as JPEG or PNG.. The prebuilt receipt model is designed to extract merchant name, transaction date, and total amount from receipts with high accuracy. Additionally, ensuring input documents are in supported formats like JPEG or PNG allows the service to perform automatic rotation and skew correction during OCR. Together, these actions meet the requirements for field extraction and handling of rotated or skewed receipts.

Answer

Set the 'pages' parameter to include all pages in the receipt.

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Enable the 'ocr.rotationCorrection' option in the analyze request.

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Use the general document model and then parse the fields manually.

Question 2

You are using Azure AI Document Intelligence to extract data from a batch of invoices. You need to retrieve the extracted fields such as invoice ID, customer name, and total amount. Which API endpoint should you call to get the analysis results after submitting the document?

Accepted Answer

GET /documentModels/{modelId}/analyzeResults/{resultId}. After submitting a document to the analyze endpoint, the service returns a result ID. To retrieve the extracted fields, you must call the GET /documentModels/{modelId}/analyzeResults/{resultId} endpoint. This returns the analysis results, including invoice ID, customer name, total amount, and confidence scores. It is the correct way to poll for and obtain the results in the asynchronous API workflow.

Answer

GET /documentModels/{modelId}/analyze

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POST /documentModels/{modelId}/analyzeResults

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POST /documentModels/{modelId}/analyze

Question 3

You are developing a custom extraction model using Azure AI Document Intelligence Studio. Your training dataset consists of multi-page invoices with varying structures, and some fields appear only on the final page. How should you structure your tagging configuration?

Accepted Answer

Tag the fields on whatever page they appear across the sample documents in Document Intelligence Studio.. In Document Intelligence Studio, you tag fields on the pages where they actually appear across your sample documents — the labeling tool is page-aware and stores the page number alongside each field's bounding region. This lets the trained model learn that, for example, 'Total' appears on page 1 in some invoices and page 3 in others. You do not need to split documents or build orchestration models just because fields appear on different pages.

Answer

Create a separate custom model for the final page and chain them using an orchestration model.

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Split every multi-page invoice into individual single-page documents before uploading.

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Define all late-appearing fields as optional metadata parameters in the training configuration JSON file.

Question 4

You are configuring an Azure AI Document Intelligence orchestration model to automatically classify and extract data from incoming customer correspondence, which includes invoices, work orders, and purchase orders. One of the component models is failing to classify documents correctly. How should you resolve this issue?

Accepted Answer

Retrain the failing component custom model with more distinct sample documents that represent the specific document class.. The most effective way to resolve misclassification by a component model in an orchestration model is to retrain that custom model with more distinct sample documents that clearly represent the specific document class. This improves the model's ability to differentiate between classes.

Answer

Modify the orchestration model routing threshold in the Azure portal settings.

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Switch the orchestration model from a template-based classifier to a neural classifier using the REST API.

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Update the API version to bypass classification and force direct extraction via the general document model.

Question 5

Your team needs to extract data from a large volume of standardized tax forms using Azure AI Document Intelligence. The forms have a fixed layout where fields are always in the exact same location. Which model type provides the fastest training and highest accuracy for this specific use case?

Accepted Answer

Custom template model. A custom template model is designed for documents with a consistent, fixed layout, and it trains quickly because it relies on layout/template matching rather than deep semantic learning. For standardized tax forms where fields are always in the same location, the template model achieves high accuracy with minimal training data and fast training time. This is the classic use case Microsoft documents for the template model type.

Answer

Prebuilt receipt model

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Custom neural model

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General document model

Question 6

You are configuring an Azure AI Document Intelligence prebuilt model to extract key-value pairs from standard invoices. Which API operation should your application call to process the document asynchronously?

Accepted Answer

POST /documentintelligence/documentModels/prebuilt-invoice:analyze?api-version=2024-02-29-preview. The correct async analyze operation for a prebuilt model in the 2024-02-29-preview API version is POST /documentintelligence/documentModels/{modelId}:analyze, where {modelId} is prebuilt-invoice. This returns a 202 Accepted with an Operation-Location header that your app polls until the analysis completes. The :analyze suffix is the long-running-operation (LRO) pattern used by Document Intelligence.

Answer

POST /documentintelligence/documentModels/prebuilt-invoice:train?api-version=2024-02-29-preview

Answer

POST /documentintelligence/models/prebuilt-invoice/analyze?api-version=2024-02-29-preview

Answer

POST /formrecognizer/v2.1/prebuilt/invoice/analyze

Question 7

You are integrating Azure AI Document Intelligence into a web application. You call the analyzeDocument API and receive a 202 Accepted response. What is the correct pattern for your application to retrieve the final extraction results?

Accepted Answer

Poll the URL provided in the Operation-Location response header until the status returns 'succeeded', then fetch the results.. The analyzeDocument API is asynchronous: a 202 Accepted response means the request was accepted and processing is ongoing. The response includes an Operation-Location header with a URL that the client must poll until the operation status becomes 'succeeded', after which the results can be fetched from that same URL. This polling pattern is the documented and correct way to retrieve extraction results.

Answer

Wait for exactly 60 seconds and then send a GET request to the original analyze endpoint.

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Immediately parse the response body of the 202 Accepted response for the extracted JSON data.

Answer

Open a WebSocket connection to the streaming endpoint specified in the WebSocket-Location header.

Question 8

Which prebuilt Azure AI Document Intelligence model should you use to extract line items, merchant name, transaction date, and total tax from point-of-sale purchase receipts?

Accepted Answer

prebuilt-receipt. The prebuilt-receipt model is designed to extract key fields from sales receipts, including line items, merchant name, transaction date, and total tax. It is specifically trained on receipt layouts and is the correct choice for point-of-sale purchase receipts.

Answer

prebuilt-invoice

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prebuilt-idDocument

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prebuilt-businessCard

Question 9

You are building an Azure AI Document Intelligence solution to process complex financial statements. Due to varying layouts across different financial institutions, you decide to use a custom neural model instead of a custom template model. What is a key architectural advantage of the custom neural model in this scenario?

Accepted Answer

It supports cross-page tables and varying visual layouts without requiring strict coordinate alignment.. The custom neural model in Azure AI Document Intelligence is designed to handle complex, variable layouts, including cross-page tables and differing visual structures, without requiring strict coordinate alignment. This makes it ideal for financial statements from different institutions where layouts vary significantly. It leverages deep learning to understand document structure and extract fields accurately even when the position of data changes.

Answer

It requires significantly fewer training documents than a template model to achieve high accuracy.

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It can be trained entirely unsupervised without requiring any human-labeled training documents.

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It can extract data from scanned documents without running Optical Character Recognition (OCR) first.

Question 10

You are using Azure AI Document Intelligence to extract information from identity documents, such as passports and driver licenses. Which prebuilt model should you select?

Accepted Answer

prebuilt-idDocument. The prebuilt-idDocument model is specifically designed to extract key information from identity documents such as passports, driver licenses, and U.S. state IDs. It returns structured fields like name, date of birth, document number, and expiration date. This is the only prebuilt model among the options that targets identity documents.

Answer

prebuilt-tax.us.w2

Answer

prebuilt-W2

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prebuilt-businessCard

Question 11

Your company processes business cards from international clients. Which prebuilt Azure AI Document Intelligence model should you use to extract names, company names, emails, and phone numbers from these cards?

Accepted Answer

prebuilt-businessCard. The prebuilt-businessCard model is specifically trained to extract common fields from business cards, including names, company names, emails, and phone numbers. It is optimized for the typical layout and content of business cards, making it the correct choice for this scenario.

Answer

prebuilt-invoice

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prebuilt-layout

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prebuilt-receipt

Question 12

You are implementing cross-domain document extraction where incoming PDFs contain complex nested tables with merged cells. You need to extract these tables accurately into structured JSON. Which capability of Azure AI Document Intelligence should you rely on?

Accepted Answer

The tables collection in the analyze result, which provides rowSpan, colSpan, and cell bounding regions.. The tables collection in the Document Intelligence analyze result is the correct capability because it returns each detected table with its cells, and each cell includes rowIndex, columnIndex, rowSpan, colSpan, content, and boundingRegions/polygon coordinates. This is exactly what you need to reconstruct merged cells and nested table structure into JSON. The other collections do not preserve the grid geometry required for merged cells.

Answer

The documents collection, which converts tables into Markdown format automatically.

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The keyValuePairs collection, which automatically flattens table rows into key-value pairs.

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The styles collection, which identifies table gridlines through computer vision edge detection.

Question 13

You are training a custom template model in Azure AI Document Intelligence and notice that one of your key fields is consistently failing to extract on documents where the label text is vertically oriented. How should you address this issue?

Accepted Answer

Re-examine and ensure all instances of the field across training samples are accurately tagged with correct bounding regions in Document Intelligence Studio.. The issue is likely due to inaccurate labeling of the field in the training samples, especially for vertically oriented text. Ensuring all instances of the field are accurately tagged with correct bounding regions in Document Intelligence Studio will help the model learn to extract the field regardless of orientation.

Answer

Switch the model type from custom template to prebuilt read model.

Answer

Set the queryParameters property to 'orientation=vertical' in the training API request body.

Answer

Rotate the source PDF documents to landscape orientation prior to uploading them for training.

Question 14

You are auditing an Azure AI Document Intelligence solution for data privacy compliance. Documents contain sensitive Personally Identifiable Information (PII). When using prebuilt models or custom models, how is data handled by Microsoft regarding model training?

Accepted Answer

Customer data and custom model training data are encrypted at rest and in transit, and are never used to train Microsoft foundation models.. Microsoft's Azure AI Document Intelligence service treats customer data with strict privacy controls: all uploaded documents and custom model training data are encrypted at rest (AES-256) and in transit (TLS 1.2+), and Microsoft contractually commits that customer data is never used to train or improve Microsoft's foundation models. This aligns with Microsoft's Responsible AI and data privacy commitments, ensuring that sensitive PII remains isolated to the customer's resource.

Answer

Training data uploaded for custom models is made publicly available in the Azure AI gallery after 30 days.

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Data privacy controls require you to deploy a local container, as cloud APIs store training documents indefinitely.

Answer

All submitted documents are automatically used to retrain the global prebuilt models overnight.

Question 15

You are configuring an Azure AI Document Intelligence container for deployment in an on-premises disconnected environment. Which environment variable is mandatory for the container to start successfully and report billing usage?

Accepted Answer

EULA=accept, billing=, apikey=. Document Intelligence containers require three environment variables to start and report billing: EULA=accept (to acknowledge the license), billing= (the Azure endpoint that receives usage telemetry), and apikey= (the key for that billing endpoint). Without all three, the container will not start or will not report usage, which is mandatory even in disconnected environments because billing telemetry is sent periodically when connectivity is available.

Answer

AZURE_COGNITIVE_SERVICES_TENANT_ID

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AZURE_AI_CONNECTOR_CONNECTION_STRING

Answer

DOCUMENT_INTELLIGENCE_MODEL_CACHE_DIR