Question 579 of 945
AI-102 Plan and manage an Azure AI solution Practice Question
You are designing a solution that uses Azure AI Document Intelligence to extract data from invoices. The invoices are in various formats (PDF, TIFF, and JPEG) and languages. You need to ensure high accuracy for extraction. Which approach should you take?
⚠ Common exam trap
It's easy for candidates to assume the prebuilt invoice model (Option A) is always the best choice for invoices, but the question explicitly requires high accuracy across varied formats and languages, which demands a custom model tailored to the specific data.
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 a custom extraction model trained on a sample set of invoices covering all formats and languages.
Azure AI Document Intelligence’s custom extraction models are trained on your specific invoice samples, enabling the model to learn the unique layouts, fields, and languages present across PDF, TIFF, and JPEG formats. This approach directly addresses the need for high accuracy on varied formats and languages, as the model adapts to the exact patterns in your data rather than relying on a generic prebuilt model or brittle post-processing.
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 the prebuilt invoice model and configure language detection.
Why it's wrong here
Prebuilt models may have lower accuracy for non-standard formats.
- ✗
Use Azure AI Vision OCR to extract all text and then parse using regular expressions.
Why it's wrong here
Less accurate for structured extraction.
- ✗
Convert all invoices to a single format (e.g., PDF) before processing.
Why it's wrong here
Conversion may degrade quality.
- ✓
Use a custom extraction model trained on a sample set of invoices covering all formats and languages.
Why this is correct
Custom models adapt to specific variations.
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Last reviewed: Jun 24, 2026
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