Implement knowledge mining and information extraction solutions →easyMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are a data engineer at a university. The university wants to digitize its historical student records (paper forms) to make them searchable. The records are scanned as images (JPEG) and stored in Azure Blob Storage. Each form contains handwritten fields: student name, ID number, date of birth, and degree. You need to extract these fields and index them in Azure AI Search. The solution must use Azure AI Services and minimize manual labeling effort. Which approach should you take?
⚠ Common exam trap
A common mix-up: candidates confuse general OCR (Azure AI Vision) with form-specific extraction (Azure AI Document Intelligence), overlooking that Document Intelligence is purpose-built for structured field extraction from forms with minimal labeling.
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
✓
Use Azure AI Document Intelligence to train a custom extraction model with a few labeled samples, then deploy as a custom skill in Azure AI Search.
Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract structured fields from forms with handwritten text. By training a custom extraction model with a few labeled samples, you minimize manual labeling effort while achieving high accuracy for fields like student name, ID, date of birth, and degree. The model can then be deployed as a custom skill in Azure AI Search to index the extracted data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Azure AI Custom Vision to train a model to detect handwriting regions, then use Azure AI Vision OCR to read text.
Why it's wrong here
Requires manual labeling and two-step process; not optimal.
- ✗
Use Azure AI Search with a blob indexer and a skillset that includes OCR skill and Entity Recognition skill.
Why it's wrong here
Entity Recognition not reliable for handwritten fields without context.
- ✓
Use Azure AI Document Intelligence to train a custom extraction model with a few labeled samples, then deploy as a custom skill in Azure AI Search.
Why this is correct
Document Intelligence is designed for extraction from forms with minimal labeling.
- ✗
Use Azure AI Vision OCR to extract text from images, then use Azure AI Language to extract entities like name, date, and degree.
Why it's wrong here
Entity Recognition may not correctly identify fields without context; no structure.
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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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.