AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are designing a solution to extract customer names and addresses from scanned handwritten forms. The forms are stored as images in Azure Blob Storage. The extraction must achieve high accuracy with minimal manual review. Which combination of Azure AI services should you use?
⚠ Common exam trap
A common mix-up: candidates confuse prebuilt models (which work well for printed documents) with custom models (which are necessary for handwritten forms), or they assume OCR alone is sufficient without considering the need for structured field extraction.
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 with a custom model trained on handwritten forms
Azure AI Document Intelligence's custom model capability allows you to train a model specifically on handwritten forms, enabling it to learn the unique handwriting patterns and layout structures present in your scanned documents. This tailored approach achieves high accuracy with minimal manual review, as the model is optimized for your specific form type rather than generic invoice or receipt templates.
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 Document Intelligence with prebuilt invoice and receipt models
Why it's wrong here
Prebuilt invoice and receipt models are trained on those document schemas, so they will not reliably map handwritten form fields such as customer name and address. They are the right choice for processing standardised invoices or receipts, not bespoke handwritten forms.
- ✓
Azure AI Document Intelligence with a custom model trained on handwritten forms
Why this is correct
Azure AI Document Intelligence custom models learn your forms' specific layout and handwriting variations from labelled samples, directly satisfying the high-accuracy, minimal-review constraint. Unlike the prebuilt read model, a custom neural model handles the unpredictable field positions and cursive styles typical of scanned handwritten forms stored in Blob Storage.
- ✗
Azure AI Language Service with custom Named Entity Recognition (NER)
Why it's wrong here
Language Service NER extracts entities from text, not from images, so scanned forms must first be OCR'd and the resulting text loses layout context needed for address fields. It suits text analytics over documents; Document Intelligence handles image-to-field extraction end to end.
- ✗
Azure AI Computer Vision with OCR and Azure AI Search
Why it's wrong here
Computer Vision OCR reads printed or handwritten text but returns raw strings without field-level structure or confidence-driven extraction, so names and addresses need manual parsing. It suits general image text extraction; Document Intelligence's custom or prebuilt models map text to labelled fields directly.
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.