Question 313 of 993

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.

Related practice questions

Related AI-102 practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

Implement an agentic solution practice questions

Practise AI-102 questions linked to Implement an agentic solution.

Implement computer vision solutions practice questions

Practise AI-102 questions linked to Implement computer vision solutions.

Implement knowledge mining and information extraction solutions practice questions

Practise AI-102 questions linked to Implement knowledge mining and information extraction solutions.

Implement image and video processing solutions practice questions

Practise AI-102 questions linked to Implement image and video processing solutions.

Implement natural language processing solutions practice questions

Practise AI-102 questions linked to Implement natural language processing solutions.

Implement generative AI solutions practice questions

Practise AI-102 questions linked to Implement generative AI solutions.

Implement agentic AI solutions practice questions

Practise AI-102 questions linked to Implement agentic AI solutions.

Implement knowledge mining and document intelligence solutions practice questions

Practise AI-102 questions linked to Implement knowledge mining and document intelligence solutions.

Plan and manage an Azure AI solution practice questions

Practise AI-102 questions linked to Plan and manage an Azure AI solution.

Implement content moderation solutions practice questions

Practise AI-102 questions linked to Implement content moderation solutions.

AI-102 fundamentals practice questions

Practise AI-102 questions linked to AI-102 fundamentals.

AI-102 scenario practice questions

Practise AI-102 questions linked to AI-102 scenario.

Practice this exam

Start a free AI-102 practice session

Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.

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

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 →

How Courseiva writes practice questions · Editorial policy

Keep practising

More AI-102 practice questions

Last reviewed: Jul 4, 2026

Question Discussion

Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.

Loading comments…

Sign in to join the discussion.

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.