Question 439 of 1,020

Quick Answer

The correct answer is sales receipts from stores and restaurants, extracting merchant details, items, and totals. This is because Azure AI Document Intelligence’s prebuilt receipt model uses optical character recognition and deep learning to parse both printed and handwritten receipts, focusing specifically on the structured data fields common to retail and dining transactions. On the AI-900 exam, this question tests your understanding of how prebuilt models are specialized for distinct document types, often contrasting the receipt model with models for invoices, identity documents, or business cards. A common trap is confusing the receipt model with the invoice model—remember that receipts are point-of-sale records from stores and restaurants, while invoices are billing documents between businesses. For a quick memory tip, think of the three key extraction categories: merchant, items, and totals—or simply “MIT” for Merchant, Items, Totals.

AI-900 Practice Question: Describe features of computer vision workloads on Azure

This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

What types of documents does Azure AI Document Intelligence's prebuilt 'receipt' model extract data from?

Question 1easymultiple choice
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Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Sales receipts from stores and restaurants, extracting merchant details, items, and totals

Option B is correct because Azure AI Document Intelligence's prebuilt 'receipt' model is specifically designed to extract key information from sales receipts, such as merchant details, transaction items, and totals. It uses optical character recognition (OCR) and deep learning models to parse both printed and handwritten receipts from stores and restaurants, handling various formats and layouts.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Only digital PDF receipts with standardized formatting

    Why it's wrong here

    The receipt model handles images from cameras/scans too — not limited to digital PDFs.

  • Sales receipts from stores and restaurants, extracting merchant details, items, and totals

    Why this is correct

    The prebuilt receipt model extracts merchant name, date, line items, tax, and total from retail and restaurant receipts.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Medical receipts and prescription records only

    Why it's wrong here

    Medical documents use the health insurance card and other health models — the receipt model targets general retail/restaurant receipts.

  • Electronic bank transfer receipts for financial transactions

    Why it's wrong here

    Bank transfer records require custom models — the prebuilt receipt model targets point-of-sale retail and restaurant receipts.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may assume the receipt model is limited to a specific format or type of receipt, but it is designed for general sales receipts from stores and restaurants, not specialized documents like medical or bank records.

Detailed technical explanation

How to think about this question

Under the hood, the receipt model leverages a combination of OCR for text extraction and a transformer-based neural network to identify and structure fields like merchant name, date, tax, and line items. It can handle rotated, skewed, or low-quality images by preprocessing them with Azure's Read API before analysis. In a real-world scenario, a retail chain could use this model to automatically digitize thousands of daily paper receipts, extracting itemized purchases for expense reporting or inventory tracking.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.

TExam Day Tips

  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-900 question test?

Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Sales receipts from stores and restaurants, extracting merchant details, items, and totals — Option B is correct because Azure AI Document Intelligence's prebuilt 'receipt' model is specifically designed to extract key information from sales receipts, such as merchant details, transaction items, and totals. It uses optical character recognition (OCR) and deep learning models to parse both printed and handwritten receipts from stores and restaurants, handling various formats and layouts.

What should I do if I get this AI-900 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 11, 2026

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