Quick Answer
The correct answer is to train a custom extraction model using the labeled forms. This is necessary because Azure AI Document Intelligence’s prebuilt models cannot handle custom form layouts, and training a custom extraction model with your 10,000 labeled samples allows the service to learn the specific field locations and relationships unique to your scanned PDFs, achieving high accuracy through supervised learning. On the AI-102 exam, this scenario tests your understanding of when to choose custom model training over prebuilt or composed models—a common trap is assuming prebuilt models can be retrained, but they cannot; only custom models accept labeled training data. The key distinction is that custom extraction models require at least five labeled forms, and with 10,000 you have more than enough for robust field extraction via the REST API. Memory tip: “Custom for custom” – if your form layout is unique, you must train a custom model.
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. 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.
Your company is developing an AI-powered document processing solution using Azure AI Document Intelligence. The solution must extract data from scanned PDF forms. The forms are in a custom format not supported by prebuilt models. You have 10,000 labeled forms for training. The solution must be deployed in a region that supports Document Intelligence and must be accessible via a REST API. You need to ensure the solution can process forms with high accuracy. What should you do?
Use Azure AI Language to extract entities from the text
Why wrong: Not designed for document layout extraction.
Train a custom extraction model using the labeled forms
Custom model trained on labeled data provides high accuracy.
Use a prebuilt model and map fields manually
Why wrong: Prebuilt models do not support custom formats.
Use the Read model and write custom logic to extract fields
Why wrong: Complex and less accurate.
Answer choices
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 the labeled forms
Option B is correct because Azure AI Document Intelligence supports training custom extraction models using labeled forms, which is essential for handling custom form layouts not covered by prebuilt models. With 10,000 labeled forms, you have sufficient data to train a high-accuracy model that extracts specific fields via the REST API, meeting the deployment and accessibility requirements.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use Azure AI Language to extract entities from the text
Why it's wrong here
Not designed for document layout extraction.
Train a custom extraction model using the labeled forms
Why this is correct
Custom model trained on labeled data provides high accuracy.
Related concept
Read the scenario before looking for a memorised answer.
Use a prebuilt model and map fields manually
Why it's wrong here
Prebuilt models do not support custom formats.
Use the Read model and write custom logic to extract fields
Why it's wrong here
Complex and less accurate.
Common exam traps
The trap here is that candidates may confuse Azure AI Language's entity extraction with Document Intelligence's form extraction, or assume that a prebuilt model can be adapted via manual mapping, when in reality custom training is mandatory for unsupported formats.
Detailed technical explanation
Custom extraction models in Azure AI Document Intelligence use a neural network trained on labeled bounding boxes and field values from your forms, enabling it to learn spatial and semantic patterns unique to your custom format. The training process requires at least five labeled forms per field, but 10,000 forms allow for robust generalization and handling of variations. The model is deployed as a REST API endpoint, supporting synchronous and asynchronous processing with confidence scores per extracted field.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
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..
The correct answer is: Train a custom extraction model using the labeled forms — Option B is correct because Azure AI Document Intelligence supports training custom extraction models using labeled forms, which is essential for handling custom form layouts not covered by prebuilt models. With 10,000 labeled forms, you have sufficient data to train a high-accuracy model that extracts specific fields via the REST API, meeting the deployment and accessibility requirements.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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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