AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are using Azure AI Document Intelligence to process a large batch of PDF forms. The forms have varying layouts and handwriting. You need to extract text and key-value pairs. Which custom model type should you train?
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
✓
Custom neural model
Custom neural model. Azure AI Document Intelligence offers custom template models for forms with fixed layouts and custom neural models for forms with varying layouts and handwriting. Neural models use deep learning to handle variability in structure and handwriting, making them ideal for this scenario. Option A (Custom template model) assumes a fixed layout and fails with varying layouts. Option B (Prebuilt-layout model) is a prebuilt model that extracts text, tables, and selection marks but not customized key-value pairs for your forms. Option D (Custom composed model) is a combination of multiple models, but the primary choice for varying layouts is the neural model, not composed. Therefore, C is the best choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Custom template model
Why it's wrong here
Custom template models need consistent visual layouts, so varying layouts and handwriting defeat their fixed anchor and field-position extraction. They suit stable, uniform forms where field locations repeat. Neural models handle layout variation and handwriting, which this batch requires.
- ✗
Prebuilt-layout model
Why it's wrong here
Prebuilt-layout extracts text, tables and selection marks but returns no trained key-value pairs for your specific fields. It is tempting because it needs no training and handles varied layouts, yet key-value extraction requires a custom model; custom neural models support the handwriting and layout variation described.
- ✓
Custom neural model
Why this is correct
Custom neural models handle unstructured documents with varying layouts and mixed handwriting, generalising from labelled samples across diverse form structures. Custom template models require consistent visual layout, so they fail on the varying layouts described; the neural model extracts text and key-value pairs reliably.
- ✗
Custom composed model
Why it's wrong here
A composed model orchestrates several custom models trained on distinct form types, but the stem describes one batch with varying layouts and handwriting, not multiple known form classes. Composed models suit splitting a mixed document set by form type; here a single custom neural model handles layout variation and handwriting.
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