- A
Use the pre-built invoice model in Azure AI Document Intelligence with a regex field extraction
Why wrong: The pre-built invoice model does not support custom fields with regex; such extraction is only available in custom models within Document Intelligence. Therefore, this approach is not feasible as described.
- B
Build a custom skill in Azure AI Search using a Python regex
Building a custom skill in Azure AI Search allows you to run a Python regex on the text extracted by Document Intelligence. This is a straightforward and effective way to extract codes matching the specified pattern.
- C
Train a custom NER model in Azure AI Language
Why wrong: Custom NER in Azure AI Language requires training and may not be as precise for exact pattern matching as a regex-based approach; it is better suited for context-based entity extraction.
- D
Use Azure OpenAI GPT-4 with document vision to extract the codes
Why wrong: Azure OpenAI GPT-4 with vision could extract the codes, but it is more complex and expensive than necessary for a simple regex pattern extraction.
AI-102 Practice Question: Azure AI Document Intelligence pre-built models
This AI-102 practice question tests your understanding of implement knowledge mining and information extraction 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. A key principle to apply: azure AI Document Intelligence pre-built models. 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.
You need to extract product codes (e.g., 'PRD-12345') from scanned invoices using Azure AI Document Intelligence. The product codes always follow a pattern of three uppercase letters, a hyphen, and five digits. Which approach should you use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"always"Why it matters: Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
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
Build a custom skill in Azure AI Search using a Python regex
Option A is incorrect because the pre-built invoice model in Azure AI Document Intelligence does not support adding custom fields with regex patterns; custom field extraction with regex is only available in custom models. Option B is correct: you can build a custom skill in Azure AI Search using a Python regex to extract the product codes from the text output of Document Intelligence. This approach allows you to apply a regex pattern to the extracted content, providing accurate and flexible extraction without needing to train a model or use a large language model. Option C is less suitable because training a custom NER model requires labeled data and may not guarantee exact pattern matching. Option D is overkill for a simple regex pattern.
Key principle: Azure AI Document Intelligence pre-built models
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 pre-built invoice model in Azure AI Document Intelligence with a regex field extraction
Why it's wrong here
The pre-built invoice model does not support custom fields with regex; such extraction is only available in custom models within Document Intelligence. Therefore, this approach is not feasible as described.
- ✓
Build a custom skill in Azure AI Search using a Python regex
Why this is correct
Building a custom skill in Azure AI Search allows you to run a Python regex on the text extracted by Document Intelligence. This is a straightforward and effective way to extract codes matching the specified pattern.
Clue confirmation
The clue word "always" in the question point toward this answer.
Related concept
Azure AI Document Intelligence pre-built models
- ✗
Train a custom NER model in Azure AI Language
Why it's wrong here
Custom NER in Azure AI Language requires training and may not be as precise for exact pattern matching as a regex-based approach; it is better suited for context-based entity extraction.
- ✗
Use Azure OpenAI GPT-4 with document vision to extract the codes
Why it's wrong here
Azure OpenAI GPT-4 with vision could extract the codes, but it is more complex and expensive than necessary for a simple regex pattern extraction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often assume that the pre-built invoice model can handle custom regex fields, but in Azure AI Document Intelligence, regex field extraction is only available in custom models. The correct approach is to use a custom skill in Azure AI Search with a regex, not to rely on the pre-built model.
Detailed technical explanation
How to think about this question
The pre-built invoice model in Azure AI Document Intelligence uses a combination of OCR and layout analysis to extract key-value pairs and table data. Regex field extraction leverages the model's ability to define custom field types with regular expressions, which are applied to the OCR output to match patterns like 'PRD-12345'. This approach is efficient because it runs server-side within the Document Intelligence pipeline, avoiding the need for external services or custom code.
KKey Concepts to Remember
- Azure AI Document Intelligence pre-built models
- Azure AI Search custom skills
- Regular expressions (regex)
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
Azure AI Document Intelligence pre-built models
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. Azure AI Document Intelligence pre-built models 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.
Review azure AI Document Intelligence pre-built models, then practise related AI-102 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Azure AI Document Intelligence pre-built models.
What is the correct answer to this question?
The correct answer is: Build a custom skill in Azure AI Search using a Python regex — Option A is incorrect because the pre-built invoice model in Azure AI Document Intelligence does not support adding custom fields with regex patterns; custom field extraction with regex is only available in custom models. Option B is correct: you can build a custom skill in Azure AI Search using a Python regex to extract the product codes from the text output of Document Intelligence. This approach allows you to apply a regex pattern to the extracted content, providing accurate and flexible extraction without needing to train a model or use a large language model. Option C is less suitable because training a custom NER model requires labeled data and may not guarantee exact pattern matching. Option D is overkill for a simple regex pattern.
What should I do if I get this AI-102 question wrong?
Review azure AI Document Intelligence pre-built models, then practise related AI-102 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "always". Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
What is the key concept behind this question?
Azure AI Document Intelligence pre-built models
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Last reviewed: Jul 4, 2026
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