The answer is to train a custom model using at least five similar forms and label more examples of the specific field in the training set. This works because Azure Document Intelligence’s custom extraction models rely on supervised learning: providing additional ground-truth annotations for the target field directly teaches the model to recognize variations in handwriting, formatting, and layout, which is the most effective way to improve field accuracy. On the AI-102 exam, this scenario tests your understanding of how custom model training differs from prebuilt models—a common trap is assuming you can simply adjust confidence thresholds or use a prebuilt tax model, but those do not refine extraction for a single custom field. Remember the “five-plus rule”: for targeted accuracy gains, you need at least five labeled examples per field, and more is better. A useful memory tip is “label more, score higher”—the more varied examples you annotate for that one field, the sharper the model’s extraction becomes.
AI-102 Practice Question: Implement knowledge mining and document intelligence solutions
This AI-102 practice question tests your understanding of implement knowledge mining and document intelligence solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses Azure Document Intelligence to extract data from tax forms. They need to improve accuracy for a specific field. Which TWO actions should they take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Label more examples of the specific field in the training set
Option A is correct because labeling more examples of the specific field in the training set directly provides the custom model with additional ground-truth annotations for that field. This increases the model's ability to learn the variations in handwriting, formatting, and layout for that field, which is the most effective way to improve extraction accuracy for a targeted field in Azure Document Intelligence custom models.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Label more examples of the specific field in the training set
Why this is correct
More labeled examples improve model accuracy for that field.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the batch size in the analysis request
Why it's wrong here
Batch size does not affect accuracy.
✗
Reduce the image resolution to 200 DPI
Why it's wrong here
Lower resolution reduces OCR accuracy.
✗
Use the prebuilt-tax.us model
Why it's wrong here
Prebuilt models may not extract custom fields accurately.
✓
Train a custom model using 10 similar forms
Why this is correct
Custom models trained on similar forms yield better accuracy.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse prebuilt models with custom models, assuming that prebuilt models can be retrained or fine-tuned, when in fact they are static and cannot be customized for specific field accuracy improvements.
Detailed technical explanation
How to think about this question
Azure Document Intelligence custom models use a Transformer-based neural network that learns field-specific patterns from labeled examples. Each additional labeled instance of a field helps the model better understand positional and semantic variations, especially for fields with high variability like dates or dollar amounts. In practice, labeling at least 5-10 examples per field is recommended, but for fields with complex layouts or handwriting, 20+ examples can significantly boost accuracy.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement knowledge mining and document intelligence solutions — This question tests Implement knowledge mining and document intelligence solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Label more examples of the specific field in the training set — Option A is correct because labeling more examples of the specific field in the training set directly provides the custom model with additional ground-truth annotations for that field. This increases the model's ability to learn the variations in handwriting, formatting, and layout for that field, which is the most effective way to improve extraction accuracy for a targeted field in Azure Document Intelligence custom models.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A healthcare organization uses Azure Document Intelligence to process patient intake forms. They notice that the confidence scores for field extraction are low. What is the most likely cause?
easy
A.The document resolution is too low
B.The document layout is not analyzed
✓ C.The custom model was trained with only 10 labeled forms
D.The batch processing size is too large
Why C: Custom models in Azure Document Intelligence require a minimum of five labeled forms for training, but low confidence scores typically indicate insufficient training data. With only 10 labeled forms, the model lacks enough examples to generalize well across variations in handwriting, formatting, and field values, leading to poor extraction confidence.
Last reviewed: Jun 11, 2026
Question Discussion
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This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.
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