AI-102 Implement generative AI solutions Practice Question
Your organization uses Azure AI Document Intelligence to extract data from invoices. The extraction accuracy for total amounts is low. You have a labeled dataset of 500 invoices. You need to improve the model's accuracy for the 'total amount' field. What should you do?
⚠ Common exam trap
Microsoft often tests the misconception that adjusting confidence thresholds or adding more predefined models can improve extraction accuracy, when in fact only custom training with labeled data addresses field-specific low accuracy.
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
✓
Create a custom neural model and train it with the labeled dataset.
Azure AI Document Intelligence's custom neural model is specifically designed to improve extraction accuracy for fields like 'total amount' by training on labeled datasets. Unlike the prebuilt invoice model, a custom neural model learns the unique layout and variations in your invoices, directly addressing low accuracy for a specific field. Training with 500 labeled invoices provides sufficient data to fine-tune the model's extraction capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add additional predefined models for invoice processing.
Why it's wrong here
Predefined models don't address field-specific inaccuracies.
- ✗
Enable OCR enhancement to improve text recognition.
Why it's wrong here
OCR is already part of the pipeline; extraction is the issue.
- ✗
Increase the confidence threshold for the total amount field.
Why it's wrong here
Higher threshold reduces false positives but doesn't improve model accuracy.
- ✓
Create a custom neural model and train it with the labeled dataset.
Why this is correct
Custom neural models can be trained to improve accuracy on specific fields.
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