AI-103 Information Extraction Practice Test 3 — 15 Questions

Question 1

When building a custom template model in Azure AI Document Intelligence Studio, which THREE types of data or attributes can you extract by labeling your training documents? (Choose THREE)

Accepted Answer

Tables (including row and column structures). Option C is correct because custom template models in Azure AI Document Intelligence Studio support labeling and extracting tabular data, including the row and column structure of tables, which the service returns as structured cells with row/column indices. Option D is correct because selection marks such as checkboxes and radio buttons are a first-class labeled entity type, returned with a state of selected or unselected and a confidence score. Option E is correct because the core purpose of a custom template model is to label and extract fields, i.e., key-value pairs such as invoice number, date, or total, which are returned as named field/value pairs. Option A does not belong because facial biometric embeddings are produced by Azure AI Face, not by Document Intelligence, which analyzes document text, layout, and structure rather than identifying people. Option B does not belong because real-time audio transcription timestamps come from Azure AI Speech (for example, the Speech-to-Text API), whereas Document Intelligence processes documents and images, not live audio streams.

Answer

Facial biometric embeddings

Answer

Real-time audio transcription timestamps

Question 2

Which TWO file formats are natively supported as inputs for analysis in Azure AI Document Intelligence? (Choose TWO)

Accepted Answer

application/pdf. Azure AI Document Intelligence natively accepts application/pdf (option B) because PDF is one of its primary supported input formats, allowing both digital and scanned documents to be submitted directly to the Analyze Document operations. It also natively supports image/jpeg (option D), since JPEG is among the supported image formats (along with PNG, BMP, TIFF, and HEIF) that the service can process for OCR and document analysis. The other options are not natively supported inputs: text/html (A) is not an accepted content type, application/zip (C) is not a document format the service reads directly, and application/vnd.ms-excel (E) is an Excel spreadsheet format that Document Intelligence does not accept as a native input.

Answer

text/html

Answer

application/zip

Answer

application/vnd.ms-excel

Question 3

Which THREE features are provided by the prebuilt Layout model in Azure AI Document Intelligence? (Choose THREE)

Accepted Answer

Extraction of text lines and words with bounding polygons. Option B is correct because the prebuilt Layout model in Azure AI Document Intelligence returns OCR results as text lines and individual words, each accompanied by bounding polygons that give the precise coordinates of the text on the page. Option C is correct because the Layout model extracts table structures, including the rows, columns, and cell spans, so tabular data is returned in an organized, machine-readable form. Option E is correct because the Layout model detects selection marks such as checkboxes and radio buttons and reports their state as selected or unselected. Option A does not belong because sentiment analysis is a natural language processing capability (for example, from Azure AI Language), not a feature of the Document Intelligence Layout model. Option D does not belong because translation is handled by Azure AI Translator, whereas the Layout model only extracts and structures document content without translating it.

Answer

Automatic sentiment analysis of paragraph text

Answer

Language translation of extracted text into English

Question 4

You are troubleshooting a low confidence score on specific fields extracted by your Azure AI Document Intelligence custom template model. Which TWO actions can help improve the extraction confidence for those fields? (Choose TWO)

Accepted Answer

Ensure all training documents are of high visual quality and free from blur or heavy skew.. Option C is correct because custom template models learn field positions and visual patterns from the labeled samples, so blurry, skewed, or low-quality training documents degrade the model's ability to reliably locate and read those fields, producing low confidence scores; supplying sharp, straight, high-quality scans directly improves feature extraction and OCR accuracy. Option D is correct because low confidence on specific fields usually indicates the model has not seen enough layout variation for those fields, so adding diverse training documents that capture the different layouts, fonts, and positions where those fields appear lets the model generalize and raise its confidence. Option A is wrong because the confidence threshold in the analyzeDocument request only filters which results are returned or flagged; it does not change the model's actual extraction confidence. Option B is wrong because converting to grayscale and downsampling to 72 DPI reduces image fidelity and typically lowers OCR and extraction quality rather than improving confidence. Option E is wrong because a prebuilt business card model is a fixed, general-purpose model that cannot be trained on your specific fields or layouts, so it cannot improve confidence for your custom template fields.

Answer

Increase the confidence threshold parameter in the analyzeDocument request body to 0.99.

Answer

Convert all training PDFs to grayscale and reduce their resolution to 72 DPI to speed up processing.

Answer

Switch the model type from custom template to a prebuilt business card model.

Question 5

When analyzing documents using Azure AI Document Intelligence, which THREE properties can be found within the returned JSON result object for an extracted table cell? (Choose THREE)

Accepted Answer

columnIndex. In the Azure AI Document Intelligence analyze-result JSON, each table cell is represented with its grid position and text: option A (columnIndex) is correct because the zero-based column position of the cell within the table is returned as the columnIndex property, and option E (rowIndex) is correct because the zero-based row position is returned as the rowIndex property. Option D (content) is also correct because the cell's recognized text is returned in the content field (with spans and boundingRegions tying it to the source). Options B (audioTranscript) and C (sentimentScore) do not belong: audioTranscript relates to speech-to-text transcription and sentimentScore to language sentiment analysis, neither of which is part of the Document Intelligence table-cell schema.

Answer

audioTranscript

Answer

sentimentScore

Question 6

You are developing an application that needs to automatically extract key-value pairs and tabular data from standardized invoices without any prior labeled training data. Which Azure AI Document Intelligence model should you use?

Accepted Answer

Azure AI Document Intelligence Prebuilt Invoice model. The Prebuilt Invoice model is purpose-built for standardized invoice documents and extracts key-value pairs (vendor, invoice date, totals, line items) plus tabular data out of the box, with no labeled training data required. It is one of the Document Intelligence prebuilt models trained on millions of invoice documents, so it handles common invoice layouts immediately. The General Document model extracts text and generic key-value pairs but does not understand invoice-specific fields like InvoiceTotal or DueDate.

Answer

Azure AI Document Intelligence General Document model

Answer

Azure AI Document Intelligence Custom Extraction model

Answer

Azure AI Vision OCR feature

Question 7

You need to extract text lines, words, bounding polygons, and selection marks (such as radio buttons and checkboxes) from arbitrary documents without training. Which prebuilt model should you invoke?

Accepted Answer

Prebuilt Layout model. The prebuilt Layout model in Azure AI Document Intelligence extracts text lines, words, bounding polygons, and selection marks (checkboxes, radio buttons) from documents without any training. It is designed for general document structure extraction and is the correct choice when you need layout-level elements and selection marks from arbitrary documents. It requires no custom training and works out of the box.

Answer

Prebuilt Business Card model

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Prebuilt ID Document model

Answer

Custom Neural model

Question 8

You are migrating a custom extraction solution from a Custom Template model to a Custom Neural model in Azure AI Document Intelligence. What is a key advantage of choosing a Custom Neural model over a Custom Template model?

Accepted Answer

Custom Neural models can automatically generalize across documents with varying visual layouts and structures.. Custom Neural models in Azure AI Document Intelligence use deep learning to learn the semantic and structural patterns of documents, enabling them to generalize across varying visual layouts, fonts, and formats. This is the key advantage over Custom Template models, which rely on fixed positional anchors and fail when layouts shift. Neural models are therefore preferred for documents with high variability.

Answer

Custom Neural models only support English language documents.

Answer

Custom Neural models require zero training documents.

Answer

Custom Neural models do not support table extraction.

Question 9

You are configuring an Azure AI Document Intelligence custom extraction pipeline that processes thousands of diverse medical forms daily. You notice that extraction confidence scores for certain hand-written fields are low. What is the most effective remediation step within the Document Intelligence workflow?

Accepted Answer

Add additional labeled training documents that specifically feature diverse handwriting samples for those low-confidence fields.. The most effective remediation for low-confidence extraction of handwritten fields in a custom Document Intelligence model is to provide more labeled training data that represents the variability causing the low confidence. Handwriting is highly variable, so adding diverse handwriting samples for the specific fields helps the model learn the patterns and improve accuracy. This directly addresses the root cause—insufficient training examples for those field variations—within the Document Intelligence workflow.

Answer

Disable the OCR engine parameter in the API call to bypass text recognition steps.

Answer

Decrease the model training iterations configuration setting to prevent overfitting on handwriting.

Answer

Switch from a Custom Neural model to a Custom Template model.

Question 10

Your application uses the Azure AI Document Intelligence Prebuilt Receipt model to process customer receipts. A receipt contains a custom tip amount that is not explicitly categorized by the default prebuilt schema. How should you extract this custom value?

Accepted Answer

Use the Prebuilt Layout model alongside custom regex rules or train a Custom model to capture the custom tip field.. Prebuilt models expose a fixed schema and cannot be extended via request parameters or portal edits. To capture a field outside that schema (like a custom tip), you either use the Layout model to get raw text/lines and apply your own extraction logic (e.g., regex), or train a Custom extraction model with labeled samples that include the tip field. Option D describes both valid paths.

Answer

Call the Azure OpenAI service directly with the raw image to guess the tip amount.

Answer

Pass a custom JSON schema parameter in the Prebuilt Receipt API request body to extend its extraction fields.

Answer

Modify the prebuilt receipt model schema directly in the Azure Portal to add a new required field.

Question 11

You are designing an information extraction solution using Azure AI Document Intelligence. Which TWO features are supported by Custom Neural models? Each correct selection represents a complete solution element.

Accepted Answer

Extraction of key-value pairs and fields defined during training. Option A is correct because Custom Neural models are trained on labeled samples to extract the specific key-value pairs and fields you define during training, returning structured JSON with confidence scores for those fields. Option C is correct because Custom Neural models support table extraction, including documents with variable layouts, by learning the structure from your labeled training data. Option B is incorrect because zero-shot extraction of unseen document types without training data is not a Custom Neural capability; that is handled by prebuilt models or the newer query-field/zero-shot features, not by training a Custom Neural model. Option D is incorrect because voice-to-text transcription is a speech service capability, not a Document Intelligence extraction feature. Option E is incorrect because automatic translation into 100+ languages is provided by Azure AI Translator, not by Custom Neural models.

Answer

Zero-shot extraction of completely unseen document types without any training data

Answer

Real-time voice-to-text transcription of document contents

Answer

Automatic language translation of extracted text into 100+ languages

Question 12

Which THREE prebuilt document models are natively available out-of-the-box in Azure AI Document Intelligence? Each correct selection represents a valid prebuilt model.

Accepted Answer

Prebuilt W-2 tax form model. Azure AI Document Intelligence ships several prebuilt models for common business documents, and the prebuilt W-2 tax form model (A) is one of them, designed to extract US wage and tax statement fields such as wages, tips, and federal tax withheld. The prebuilt Invoice model (B) is also natively available and extracts key invoice fields like vendor name, invoice date, due date, line items, and amounts due. The prebuilt Receipt model (E) is likewise an out-of-the-box model that extracts merchant name, transaction date, line items, and totals from sales receipts. Options C and D are not real Document Intelligence prebuilt models: Video Sentiment analyzer is not a document analysis capability, and 3D CAD Model Analyzer does not exist in the service's prebuilt model catalog.

Answer

Prebuilt Video Sentiment analyzer

Answer

Prebuilt 3D CAD Model Analyzer

Question 13

You need to extract data from a highly varied set of tax forms where the layout changes frequently. Which approach is most efficient?

Accepted Answer

Use a Neural custom model. A Neural custom model in Azure AI Document Intelligence is trained on your own labeled data and can generalize to layout variations, making it ideal for frequently changing tax forms. It learns the structure and key-value pairs from examples rather than relying on fixed templates. This provides the most efficient extraction for highly varied documents.

Answer

Use a Template model

Answer

Use Read API

Answer

Use General Document model

Question 14

You are developing an application to extract specific fields from invoices using Azure AI Document Intelligence. Which prebuilt model should you use for this task?

Accepted Answer

prebuilt-invoice. The prebuilt-invoice model is purpose-built for invoice documents and returns structured fields such as VendorName, InvoiceDate, InvoiceTotal, DueDate, CustomerName, and line items without any training. Because the task is extracting specific fields from invoices, this prebuilt model directly matches the document type and required output. Custom models or generic layout models would require extra work and would not return invoice-specific fields out of the box.

Answer

custom-template

Answer

prebuilt-layout

Answer

prebuilt-document

Question 15

You are using Document Intelligence Studio to build a custom extraction model. You need to verify the model quality before deploying. Which metric is most critical?

Accepted Answer

F1 Score. F1 Score is the harmonic mean of precision and recall, so it captures both false positives and false negatives in a single number. For custom extraction models where missing a field and misreading a field are both costly, F1 gives the most balanced view of overall model quality before deployment.

Answer

Recall only

Answer

Precision only

Answer

Model ID