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AI-102 Plan and manage an Azure AI solution Practice Question

You are designing an AI solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle a high volume of documents with varying layouts. Which approach should you use?

⚠ Common exam trap

Many candidates assume the prebuilt invoice model (Option C) is sufficient for all invoice scenarios, but it only works for standard layouts and fails when invoices have custom fields or non-standard structures.

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

✓

Train a custom neural model with labeled invoices

Custom neural models in Azure AI Document Intelligence are designed to handle high volumes of documents with varying layouts. Unlike fixed template models, neural models learn from labeled examples and generalize across different invoice structures, making them ideal for diverse, high-volume scenarios.

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 a custom template model with fixed field positions
Why it's wrong here
Template models require consistent layouts.
✓
Train a custom neural model with labeled invoices
Why this is correct
Neural models adapt to varying layouts and scale well.
✗
Use the prebuilt invoice model
Why it's wrong here
Prebuilt models may not handle all layout variations.
✗
Extract text using OCR and then use regex parsing
Why it's wrong here
Regex is brittle and not AI-driven.

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AI-102 Plan and manage an Azure AI solution Practice Question

Why each option matters

Option-by-option breakdown

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