- A
Analysing customer satisfaction scores from post-purchase surveys
Why wrong: Survey analysis is NLP/sentiment analysis — receipt analysis extracts structured financial data from receipt images.
- B
Extracting merchant name, items, prices, tax, and totals from retail receipt images
Receipt analysis extracts structured financial fields from receipts — enabling automated expense management and bookkeeping.
- C
Verifying that a receipt matches the purchase record in a financial database
Why wrong: Purchase reconciliation is a business process — receipt analysis extracts data from the receipt image, not validates it against records.
- D
Detecting fraudulent receipts by comparing them to a known-good receipt database
Why wrong: Fraud detection is a downstream application — receipt analysis is the data extraction step, not the validation or fraud detection step.
Azure AI Document Intelligence Receipt Analysis: What Data It Extracts
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 is 'receipt analysis' in Azure AI Document Intelligence and what data does it extract?
Quick Answer
The answer is that receipt analysis in Azure AI Document Intelligence extracts merchant name, items, prices, tax, and totals from retail receipt images. This is correct because the prebuilt receipt model uses optical character recognition and deep learning to parse unstructured receipt layouts into structured key-value pairs and line items, specifically targeting the fields most commonly needed for expense tracking and accounting automation. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of prebuilt models within Document Intelligence, often appearing as a scenario where you must identify which fields a receipt analysis solution would output. A common trap is confusing receipt analysis with invoice analysis—invoices include customer addresses and payment terms, while receipts focus on point-of-sale data like itemized purchases and subtotals. To remember, think of a physical store receipt: it always lists the store name, each item with its price, and the final tax and total, which is exactly what this model extracts.
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
Extracting merchant name, items, prices, tax, and totals from retail receipt images
Receipt analysis in Azure AI Document Intelligence is a prebuilt model designed to extract key-value pairs and line items from sales receipts. Option B correctly identifies that it extracts merchant name, items, prices, tax, and totals from retail receipt images, which is the primary function of this model.
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.
- ✗
Analysing customer satisfaction scores from post-purchase surveys
Why it's wrong here
Survey analysis is NLP/sentiment analysis — receipt analysis extracts structured financial data from receipt images.
- ✓
Extracting merchant name, items, prices, tax, and totals from retail receipt images
Why this is correct
Receipt analysis extracts structured financial fields from receipts — enabling automated expense management and bookkeeping.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Verifying that a receipt matches the purchase record in a financial database
Why it's wrong here
Purchase reconciliation is a business process — receipt analysis extracts data from the receipt image, not validates it against records.
- ✗
Detecting fraudulent receipts by comparing them to a known-good receipt database
Why it's wrong here
Fraud detection is a downstream application — receipt analysis is the data extraction step, not the validation or fraud detection step.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing the extraction of receipt data with downstream tasks like validation, fraud detection, or sentiment analysis, leading candidates to select options that describe post-processing steps rather than the core capability of the receipt analysis model.
Detailed technical explanation
How to think about this question
The receipt analysis model uses optical character recognition (OCR) and deep learning to parse receipt layouts, extracting fields like MerchantName, TransactionDate, Items (with Description, Quantity, Price), Subtotal, Tax, and Total. It handles various receipt formats and languages, outputting structured JSON. A real-world scenario is automating expense reporting by extracting data from scanned receipts and feeding it into accounting systems.
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
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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: Extracting merchant name, items, prices, tax, and totals from retail receipt images — Receipt analysis in Azure AI Document Intelligence is a prebuilt model designed to extract key-value pairs and line items from sales receipts. Option B correctly identifies that it extracts merchant name, items, prices, tax, and totals from retail receipt images, which is the primary function of this model.
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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