AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is 'receipt analysis' in Azure AI Document Intelligence and what data does it extract?
⚠ Common exam trap
It's easy for candidates to confuse 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.
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.
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
This describes sentiment analysis or text analytics on survey responses, which is a language-based task, not a document-extraction task. Receipt analysis processes images of retail receipts to pull structured financial fields like tax and totals, whereas customer satisfaction scores are derived from open-ended text or numeric ratings. Mixing these conflates two distinct Azure AI services: Document Intelligence for visual document extraction and Azure AI Language for textual sentiment analysis.
- ✓
Extracting merchant name, items, prices, tax, and totals from retail receipt images
Why this is correct
Extracting merchant name, items, prices, tax, and totals from retail receipt images is the core purpose of Azure AI Document Intelligence's prebuilt receipt model. It uses OCR and deep learning to locate and transcribe these structured financial fields, handling variations in receipt layouts and currencies. This extracted data directly enables automated expense reporting, bookkeeping, and accounting workflows.
- ✗
Verifying that a receipt matches the purchase record in a financial database
Why it's wrong here
This is purchase reconciliation, a business process that compares extracted receipt fields against records in an external financial system. The prebuilt receipt model in Azure AI Document Intelligence only reads and normalizes key-value pairs from the image; it has no connectivity to financial databases and performs no matching or verification. Reconciling a receipt to a purchase record is a separate application built on top of the extracted data.
- ✗
Detecting fraudulent receipts by comparing them to a known-good receipt database
Why it's wrong here
This describes a downstream fraud-detection use case, not the core receipt-analysis capability. Receipt analysis in Azure AI Document Intelligence extracts structured data such as merchant names, line items, and totals from images; it does not maintain a 'known-good' receipt database or compute anomaly scores against historical receipts. Comparing receipts for fraud requires an additional, custom-built decisioning layer beyond the extraction service.
Go deeper
Related to this question
Learn chapter
What is Artificial Intelligence?
Key term
Document intelligence
Document intelligence is a cloud-based service that uses artificial intelligence to extract, analyze, and understand information from documents like forms, invoices, and receipts automatically.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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