AI-103 Information Extraction Practice Test 4 — 15 Questions

Question 1

Your custom extraction model is failing to identify fields on a new version of a document. You have labeled 50 documents. What is the most likely solution?

Accepted Answer

Add more training documents with the new layout. When a custom extraction model fails on a new document layout, the most likely fix is to add more training documents that represent the new layout so the model can learn the additional variations. Custom models (especially neural) improve with more diverse, labeled examples covering the layouts they must handle; 50 documents of the old layout are insufficient for the new version. Retraining with representative samples is the standard remedy.

Answer

Change the API version to v2.1

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Switch to a template model

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Increase the confidence threshold

Question 2

Which file format is NOT natively supported for training custom models in Azure AI Document Intelligence?

Accepted Answer

DOCX. Azure AI Document Intelligence custom model training accepts PDF, JPEG, PNG, BMP, TIFF, and HEIF image formats, but DOCX (Word) files are not natively supported as training inputs. To train on Word content, you must first convert DOCX to PDF or render pages as images before uploading to the labeled training set. This is why DOCX is the correct answer to 'which is NOT natively supported.'

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PDF

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JPEG

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BMP

Question 3

What is the primary function of the 'layout' model in Azure AI Document Intelligence?

Accepted Answer

To extract text, tables, and document structure. The layout model in Azure AI Document Intelligence extracts text, tables, selection marks, and document structure (such as paragraphs, headings, and reading order) without requiring a trained custom model. It is the general-purpose model for understanding a document's structural layout, making it the correct answer.

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To translate text into different languages

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To classify document types

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To extract key-value pairs from receipts

Question 4

When labeling documents for a custom extraction model, what happens if you label the same field inconsistently across documents?

Accepted Answer

Model accuracy will decrease. Inconsistent labeling of the same field across training documents introduces conflicting ground truth, which degrades the model's ability to learn a stable pattern. The neural trainer will still complete, but precision and recall for that field drop because the model receives contradictory signals. Consistency is a core labeling best practice in Document Intelligence Studio.

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The training will fail immediately

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No impact

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The model will automatically correct the labels

Question 5

You are processing large PDF documents. You only need the text in the table of contents. What is the most cost-effective approach?

Accepted Answer

Use the 'pages' query parameter. The 'pages' query parameter in Azure AI Document Intelligence allows you to specify which pages to process, such as a single page or a range. If you only need the table of contents, you can identify the page number(s) containing it and pass the 'pages' parameter to limit processing to those pages. This reduces the number of pages processed, thereby lowering cost, since Document Intelligence pricing is based on the number of pages analyzed.

Answer

Use a custom template model

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Split the PDF into individual pages before uploading

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Process the entire PDF

Question 6

Which of the following is a key advantage of using a 'prebuilt' model over a 'custom' model?

Accepted Answer

No training required. A prebuilt model is trained by the provider on a broad corpus of common document types, so it can be used immediately without any labeled training data or training cycle. This 'no training required' characteristic is its defining advantage — you get fast time-to-value and predictable behavior out of the box. Custom models, by contrast, require you to label documents and train, which costs time and effort.

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Higher accuracy for niche documents

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Lower cost per page

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Ability to define custom fields

Question 7

You need to extract data from a document that includes handwritten signatures and printed text. Which capability is required?

Accepted Answer

Handwritten OCR (Read). Handwritten OCR (Read) is the Azure AI Vision capability that recognizes both printed and handwritten text in documents and images, making it the required feature when signatures and handwriting are present. The Read API returns extracted text with bounding boxes and confidence, supporting mixed print and handwriting. This directly satisfies the requirement to extract data from documents containing handwritten signatures and printed text.

Answer

Object Detection

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Custom Template model

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Form Recognizer 2.0 API

Question 8

You are automating document processing. You want to trigger a workflow only after a Document Intelligence operation finishes. What is the standard way to implement this?

Accepted Answer

Poll the 'operation-location' URL. The Document Intelligence analyze operation is asynchronous: it returns a 202 Accepted response with an 'Operation-Location' header containing a URL. The standard way to detect completion is to poll that URL until the status field returns 'succeeded' or 'failed'. This is the documented pattern for all long-running Document Intelligence operations.

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Use a fixed-delay thread sleep

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Use a WebSocket

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Wait for a push notification

Question 9

Your company requires strict data privacy. Where should you host your Document Intelligence resources to ensure data remains in a specific region?

Accepted Answer

Regional Azure resource. To guarantee that Document Intelligence data (documents, extracted results, and processing) stays within a specific geography, you must deploy a regional Azure resource in the desired region. Regional resources pin both compute and data residency to that region, satisfying strict data-privacy and sovereignty requirements. Global or multi-region endpoints may replicate or route data outside the chosen geography, violating the requirement.

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Any available region

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Global service endpoint

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Public multi-region endpoint

Question 10

You are extracting data from tables. The layout model is missing specific column headers. How can you improve the results?

Accepted Answer

Use a Custom Neural model. When the layout model misses specific column headers, a Custom Neural model can be trained with labeled examples that include those headers, allowing the model to learn the specific structure and improve extraction. Custom Neural models are designed for extracting structured data from documents with varying layouts and can be tailored to recognize custom fields, including headers that the prebuilt layout model might overlook.

Answer

Increase image resolution

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Use the Read API

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Enable 'document-classify' feature

Question 11

What is the primary purpose of the 'selection marks' detection in the layout model?

Accepted Answer

To detect checkboxes and radio buttons. Selection marks in Azure AI Document Intelligence's layout model detect and extract checkboxes and radio buttons (and their selected/unselected state) from documents. The model returns each mark with a confidence score and a state of 'selected' or 'unselected', which is essential for processing forms, surveys, and questionnaires. OCR, table borders, and signatures are handled by other capabilities.

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To OCR printed text

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To identify table borders

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To find handwritten signatures

Question 12

Which tool provides a graphical user interface for labeling documents for custom models?

Accepted Answer

Document Intelligence Studio. Document Intelligence Studio is the web-based graphical interface provided by Azure that lets you visually label documents, define fields, train custom models, and test them against sample files. It is specifically designed for the labeling and project-management workflow of custom extraction and custom classification models, so it is the correct answer for a GUI-based labeling tool.

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Azure CLI

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Azure Storage Explorer

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Visual Studio Code

Question 13

You are integrating Document Intelligence with Azure Blob Storage. You need to provide the model with a set of documents for training. How should you prepare the data?

Accepted Answer

Upload files and corresponding .labels.json and .ocr.json files. To train a Document Intelligence model with documents in Azure Blob Storage, you must upload the documents along with their corresponding .labels.json and .ocr.json files. The .labels.json file contains the labeled data (field names and values), and the .ocr.json file contains the OCR results for the document. These files are generated using the Document Intelligence Studio or SDK and are required for training a custom model.

Answer

Convert all files to CSV

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Store files in an Azure SQL database

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Upload raw files only

Question 14

Your application is receiving 429 errors from the Document Intelligence service. What does this indicate?

Accepted Answer

Rate limit exceeded. HTTP 429 'Too Many Requests' indicates the client has exceeded the rate limit or quota for the Document Intelligence service. Azure Cognitive Services enforce per-second and per-minute transaction limits based on the pricing tier (e.g., S0 has specific TPS limits). The correct response is to implement retry with exponential backoff and/or request a quota increase.

Answer

Authentication failed

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Server-side error

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The request is malformed

Question 15

You are using Document Intelligence to classify documents into different types (e.g., invoices, bank statements). Which model is most appropriate?

Accepted Answer

Custom classification model. A custom classification model is purpose-built to categorize documents into user-defined types (e.g., invoice vs. bank statement) before or alongside extraction. It is trained on labeled examples of each document class and returns a document type with confidence. Prebuilt-invoice extracts fields from invoices but does not classify arbitrary document types.

Answer

Prebuilt-invoice

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Read model

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Custom extraction model