Question 267 of 1,020

Quick Answer

The correct answer is that Azure AI Document Intelligence’s custom extraction model is used for training on your labeled documents to extract business-specific fields not covered by prebuilt models. This is correct because the service allows you to upload your own forms—such as specialized invoices, contracts, or medical records—and manually label the unique data fields you need, like “policy number” or “diagnosis code,” which standard prebuilt models cannot recognize. On the AI-900 exam, this concept tests your understanding of when to choose custom over prebuilt models; a common trap is assuming prebuilt models can handle any document type, but they only cover common fields like dates and totals. Remember that custom extraction is your go-to for domain-specific data that isn’t universally standardized. A helpful memory tip: think “custom for the uncommon”—if your business form has a field your competitor’s doesn’t, you need a custom extraction model.

AI-900 Practice Question: Describe features of computer vision workloads on Azure

This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. 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.

What is Azure AI Document Intelligence's 'custom extraction model' used for?

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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

Training on your labeled documents to extract business-specific fields not covered by prebuilt models

Azure AI Document Intelligence's custom extraction model is correct because it allows you to train a model on your own labeled documents to extract fields that are specific to your business domain and not covered by prebuilt models. This is essential for processing specialized forms like invoices, contracts, or medical records that have unique data fields.

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.

  • Automatically generating new document templates from existing forms

    Why it's wrong here

    Template creation is document design — custom extraction models learn to extract specific fields from your existing document types.

  • Training on your labeled documents to extract business-specific fields not covered by prebuilt models

    Why this is correct

    Custom extraction models handle unique business forms — labeled with your specific field names to train field extraction for your document types.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Translating documents into multiple languages simultaneously

    Why it's wrong here

    Document translation is Azure AI Translator's capability — custom extraction extracts structured data from documents.

  • Redacting sensitive information from documents automatically

    Why it's wrong here

    PII redaction is a separate capability — custom extraction models extract specific field values from business forms.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse custom extraction models with template generation or translation, assuming Document Intelligence can create templates or translate text, when in reality it is strictly for extraction and classification of document content.

Detailed technical explanation

How to think about this question

Under the hood, custom extraction models use a combination of optical character recognition (OCR) and deep learning to identify and extract key-value pairs, tables, and selection marks from documents. The model is trained on a set of labeled documents where you manually tag the fields you want to extract, and it learns to generalize to new documents. In a real-world scenario, a healthcare provider could train a custom model to extract patient ID, diagnosis codes, and treatment dates from medical forms that prebuilt models cannot handle.

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.

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FAQ

Questions learners often ask

What does this AI-900 question test?

Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Training on your labeled documents to extract business-specific fields not covered by prebuilt models — Azure AI Document Intelligence's custom extraction model is correct because it allows you to train a model on your own labeled documents to extract fields that are specific to your business domain and not covered by prebuilt models. This is essential for processing specialized forms like invoices, contracts, or medical records that have unique data fields.

What should I do if I get this AI-900 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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Last reviewed: Jun 11, 2026

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