AI-102 Plan and manage an Azure AI solution Practice Question
A company is planning to use Azure AI Document Intelligence to extract data from invoices. The solution must handle variations in invoice formats. Which TWO actions should be taken to improve accuracy?
⚠ Common exam trap
A common mix-up: candidates assume prebuilt models are sufficient for all invoice formats, ignoring that custom models with diverse labeled samples are necessary to handle layout variations and achieve high 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
✓
Include samples of different layouts in the training set.
Including samples of different invoice layouts in the training set enables the custom model to learn variations in structure, field placement, and formatting. This improves the model's ability to generalize across diverse invoice formats, reducing extraction errors. Azure AI Document Intelligence custom models require labeled training data to adapt to specific document types, and diverse samples directly address format variability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Avoid using custom models; rely on prebuilt models only.
Why it's wrong here
Prebuilt models may not cover all fields.
- ✓
Include samples of different layouts in the training set.
Why this is correct
Diverse layouts improve generalization.
- ✗
Use a large number of unlabeled samples.
Why it's wrong here
Labeled data is required for custom training.
- ✗
Use a single prebuilt model for all invoices.
Why it's wrong here
Prebuilt models may not handle all variations.
- ✓
Train a custom model with labeled invoice samples.
Why this is correct
Custom models adapt to specific formats.
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