AI-103 Information Extraction Practice Test 3 — 15 Questions

Question 1

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?

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

Accepted Answer

Add additional labeled training documents that specifically feature diverse handwriting samples for those low-confidence fields.. Adding more high-quality, representative labeled training samples containing diverse handwriting styles directly improves custom model performance and confidence on ambiguous fields.

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 2

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.. When prebuilt models do not cover specific custom fields, you can combine the Prebuilt Read/Layout model output with custom parsing logic, or train a custom model if needed, but for prebuilt models, leveraging general key-value pairs or post-processing layout text is standard.

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 3

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. Custom Neural models support extracting both individual key-value fields and complex tables from unstructured or semi-structured documents.

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 4

When composing multiple individual Custom Neural models into a single composed model in Azure AI Document Intelligence, which THREE behaviors or characteristics apply? Each correct selection represents a valid aspect.

Accepted Answer

All sub-models included in a composed model must share the same major API version.. Composed models route documents automatically to the correct underlying model, share a single endpoint, and require all component models to be built using the same major API version.

Answer

Composed models require a separate Azure resource endpoint for each individual sub-model.

Answer

Composed models support an unlimited number of sub-models without any performance impact.

Question 5

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 provides specific prebuilt models for invoices, receipts, and W-2 tax forms, among others.

Answer

Prebuilt Video Sentiment analyzer

Answer

Prebuilt 3D CAD Model Analyzer

Question 6

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 is best suited for documents with high visual variability.

Answer

Use a Template model

Answer

Use Read API

Answer

Use General Document model

Question 7

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 specifically designed to extract key-value pairs and line items from invoices.

Answer

custom-template

Answer

prebuilt-layout

Answer

prebuilt-document

Question 8

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. The F1 score provides a balance between precision and recall, serving as the primary indicator of model quality.

Answer

Recall only

Answer

Precision only

Answer

Model ID

Question 9

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. Adding more training samples that include the new document variations improves the model's robustness.

Answer

Change the API version to v2.1

Answer

Switch to a template model

Answer

Increase the confidence threshold

Question 10

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

Accepted Answer

DOCX. Azure AI Document Intelligence supports PDF, JPEG, PNG, TIFF, and BMP, but not native Word documents (DOCX) for training sets without conversion.

Answer

PDF

Answer

JPEG

Answer

BMP

Question 11

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 identifies text, tables, selection marks, and structure within a document.

Answer

To translate text into different languages

Answer

To classify document types

Answer

To extract key-value pairs from receipts

Question 12

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 introduces noise into the training data, which reduces the final F1 score of the model.

Answer

The training will fail immediately

Answer

No impact

Answer

The model will automatically correct the labels

Question 13

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. Using the 'pages' parameter in the API call allows you to restrict processing to specific pages, saving costs.

Answer

Use a custom template model

Answer

Split the PDF into individual pages before uploading

Answer

Process the entire PDF

Question 14

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

Accepted Answer

No training required. Prebuilt models are ready to use immediately without the need for training data.

Answer

Higher accuracy for niche documents

Answer

Lower cost per page

Answer

Ability to define custom fields

Question 15

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

Accepted Answer

Handwritten OCR (Read). The Read capability, which is part of the Document Intelligence service, supports both machine-printed text and handwriting.

Answer

Object Detection

Answer

Custom Template model

Answer

Form Recognizer 2.0 API