Implement knowledge mining and information extraction solutions →mediumMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Your organization has a large set of PDF invoices stored in Azure Blob Storage. You need to extract line-item details (product names, quantities, prices) and store them in Azure SQL Database for downstream reporting. The invoices have varied layouts. Which Azure AI service should you use?
⚠ Common exam trap
Watch out — candidates often confuse Azure AI Computer Vision's OCR capability with Document Intelligence's document understanding, leading them to choose Computer Vision for any text extraction task, even when the requirement involves structured data extraction from varied-layout documents like invoices.
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
✓
Azure AI Document Intelligence
Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data (like line-item details) from documents with varied layouts, such as invoices. Its prebuilt invoice model can parse product names, quantities, and prices from PDFs without requiring manual template configuration, and it outputs the data in a structured format that can be ingested into Azure SQL Database.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure AI Computer Vision
Why it's wrong here
Computer Vision provides OCR but does not extract structured line items from varied layouts.
- ✗
Azure AI Language Service
Why it's wrong here
Language Service is for text analytics and NLP, not document extraction.
- ✗
Azure AI Search
Why it's wrong here
Azure AI Search indexes data for search; it does not extract data from source documents.
- ✓
Azure AI Document Intelligence
Why this is correct
Document Intelligence can extract structured data from invoices with varied layouts using prebuilt invoice models.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Same concept, more angles
1 more way this is tested on AI-102
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Variation 1. You are building a solution to extract key information from invoices using Azure AI Document Intelligence. The invoices contain fields such as invoice number, date, total amount, and line items. However, the model is not correctly extracting the line items. Which prebuilt model should you use?
medium- A.Prebuilt-receipt model
- B.Prebuilt-idDocument model
- ✓ C.Prebuilt-invoice model
- D.Prebuilt-layout model
Why C: The prebuilt-invoice model is specifically trained to extract key fields from invoices, including invoice number, date, total amount, and line items. Unlike other prebuilt models, it has dedicated field extraction for line item details such as description, quantity, unit price, and total, making it the correct choice for this scenario.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.