The correct answer is to train a custom extraction model using labeled sample invoices. This is because Azure AI Document Intelligence’s prebuilt invoice models can only recognize standard fields, but a custom extraction model learns to identify unique fields like ‘purchase order number’ by analyzing labeled examples, even when that field appears in varying positions across documents. On the AI-102 exam, this question tests your understanding of when to move from prebuilt to custom models—a common trap is assuming you can simply add a field to a prebuilt model or use a different prebuilt model, but only custom training with labeled data handles non-standard layouts. A strong memory tip: if the field is not in the prebuilt schema, you must “label to enable”—labeled samples are the key to teaching the model new extraction patterns.
AI-102 Plan and manage an Azure AI solution Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Your organization uses Azure AI Document Intelligence to extract data from invoices. The solution must identify custom fields not present in the prebuilt models, such as 'purchase order number' located in varying positions across documents. What should you do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Train a custom extraction model using labeled sample invoices.
Option D is correct because Azure AI Document Intelligence (formerly Form Recognizer) allows you to train a custom extraction model using labeled sample invoices. This approach enables the model to learn custom fields like 'purchase order number' that appear in varying positions, which prebuilt models cannot handle. By providing labeled examples, the model generalizes to extract the field accurately from new documents.
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.
✗
Use the layout model and apply manual post-processing.
Why it's wrong here
Layout model does not label fields.
✗
Use Azure AI Forms Recognizer with prebuilt receipt model.
Why it's wrong here
Receipt model is for receipts, not invoices.
✗
Use the prebuilt invoice model with field merging.
Why it's wrong here
Prebuilt model may not include purchase order number.
✓
Train a custom extraction model using labeled sample invoices.
Why this is correct
Custom models learn to extract user-defined fields.
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 may assume the prebuilt invoice model can be extended with custom fields via configuration or merging, but Azure AI Document Intelligence requires explicit custom model training to recognize fields not present in prebuilt schemas.
Detailed technical explanation
How to think about this question
Custom extraction models in Azure AI Document Intelligence use a neural network trained on labeled samples to map text regions to field names, even when the field appears in different locations across documents. The training process requires at least five labeled samples per field and supports both key-value pairs and selection marks. In practice, this is essential for enterprise invoice processing where fields like 'purchase order number' may be placed in headers, footers, or free-form text areas.
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.
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Train a custom extraction model using labeled sample invoices. — Option D is correct because Azure AI Document Intelligence (formerly Form Recognizer) allows you to train a custom extraction model using labeled sample invoices. This approach enables the model to learn custom fields like 'purchase order number' that appear in varying positions, which prebuilt models cannot handle. By providing labeled examples, the model generalizes to extract the field accurately from new documents.
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.
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Question Discussion
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