AI-102 Practice Question: Implement knowledge mining and document intelligence solutions
A company uses Azure Document Intelligence to process purchase orders. They have trained a custom model with 10 labeled samples and deployed it as 'purchaseOrderModel'. When analyzing a new purchase order, the extracted 'TotalAmount' field is often incorrect. The company wants to improve the model's accuracy for this field. What should they do?
⚠ Common exam trap
The trap here is assuming that confidence thresholds or prebuilt models can be tweaked to improve a custom model's field accuracy, when the real fix is more and better training 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
✓
Retrain the model with additional labeled samples that include variations of the 'TotalAmount' field.
Custom model accuracy in Azure Document Intelligence depends heavily on the quality and quantity of labeled training data. When a specific field like TotalAmount is frequently misidentified, the most effective action is to retrain with more labeled examples that capture the field's variations. This helps the model learn the patterns and contexts associated with that field, leading to better generalization on new documents.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Retrain the model with additional labeled samples that include variations of the 'TotalAmount' field.
Why this is correct
This is correct because custom model accuracy improves with more diverse labeled data. Adding samples that cover different formats, locations, and contexts of the TotalAmount field helps the model generalize better. Azure Document Intelligence learns from labeled examples, so increasing the quantity and variety of training data directly addresses the field's extraction accuracy.
- ✗
Switch to the prebuilt invoice model, which automatically extracts total amounts from purchase orders.
Why it's wrong here
The prebuilt invoice model is designed for invoices, not purchase orders. While invoices and purchase orders share some fields, the prebuilt model may not correctly identify the TotalAmount field in a purchase order format, especially if the layout differs. Using a prebuilt model would likely reduce accuracy for this specific document type and field.
- ✗
Increase the model's confidence threshold for the 'TotalAmount' field in the project settings.
Why it's wrong here
Azure Document Intelligence does not provide a per-field confidence threshold setting that can be adjusted to improve accuracy. Confidence scores are output by the model, not used as input thresholds. While you can filter results by confidence in your application, that does not improve the model's extraction accuracy; it only affects which results you accept.
- ✗
Add a labeled sample where the 'TotalAmount' field is left blank to teach the model to ignore missing values.
Why it's wrong here
Labeling a sample with a blank TotalAmount field does not teach the model to extract the field correctly; it teaches the model that the field may be absent. This could cause the model to miss the field in documents where it is present. To improve extraction, you need positive examples that show the field with correct labels, not negative examples.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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