- A
Azure Functions to parse and validate the extracted data.
Why wrong: Functions require custom code; not the most integrated approach.
- B
Azure AI Document Intelligence custom extraction model with a defined schema.
Custom extraction models allow defining a schema and validating extracted data.
- C
Azure AI Document Intelligence prebuilt model for invoices.
Why wrong: Prebuilt models have fixed schemas not customizable.
- D
Azure Logic Apps with a JSON schema validation step.
Why wrong: Logic Apps can validate JSON but not natively define extraction schemas.
Question 781 of 993
AI-102 Custom extraction model Practice Question
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. A key principle to apply: custom extraction model. 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 are planning an Azure AI solution that uses Azure AI Document Intelligence to extract data from scanned PDFs. The solution must support both structured and unstructured documents. The extracted data must be validated against a predefined schema before being stored in Azure Cosmos DB. What should you use for schema validation?
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
✓
Azure AI Document Intelligence custom extraction model with a defined schema.
Option B is correct because Azure AI Document Intelligence custom extraction models allow you to define a schema (field names, types, and relationships) that the service uses to extract data. The model enforces this schema during extraction—only fields defined in the schema are extracted, and missing fields are returned as null—effectively validating the extracted data against your predefined structure before it is stored in Azure Cosmos DB. This built-in constraint eliminates the need for additional validation logic.
Key principle: Custom extraction model
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Functions to parse and validate the extracted data.
Why it's wrong here
Functions require custom code; not the most integrated approach.
- ✓
Azure AI Document Intelligence custom extraction model with a defined schema.
Why this is correct
Custom extraction models allow defining a schema and validating extracted data.
Related concept
Custom extraction model
- ✗
Azure AI Document Intelligence prebuilt model for invoices.
Why it's wrong here
Prebuilt models have fixed schemas not customizable.
- ✗
Azure Logic Apps with a JSON schema validation step.
Why it's wrong here
Logic Apps can validate JSON but not natively define extraction schemas.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that candidates often assume prebuilt models (like invoices) can enforce a custom schema, but they only extract fields relevant to their specific document type and cannot enforce a user-defined schema across both structured and unstructured documents. Conversely, a custom extraction model lets you define and enforce the exact schema required for validation.
Detailed technical explanation
How to think about this question
Azure AI Document Intelligence custom extraction models use a labeled dataset to train a model that maps document fields to a user-defined schema, which includes field types (e.g., string, number, date) and optional validation rules (e.g., regex patterns). During extraction, the service automatically validates extracted values against this schema and can flag or reject non-conforming data before it is output. In a real-world scenario, this is critical for compliance-heavy industries like finance or healthcare, where extracted data must match a strict schema (e.g., invoice line items with currency codes) to avoid downstream errors in Cosmos DB.
KKey Concepts to Remember
- Custom extraction model
- Schema validation
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
Custom extraction model
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. Custom extraction model 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
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FAQ
Questions learners often ask
What does this AI-102 question test?
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Custom extraction model.
What is the correct answer to this question?
The correct answer is: Azure AI Document Intelligence custom extraction model with a defined schema. — Option B is correct because Azure AI Document Intelligence custom extraction models allow you to define a schema (field names, types, and relationships) that the service uses to extract data. The model enforces this schema during extraction—only fields defined in the schema are extracted, and missing fields are returned as null—effectively validating the extracted data against your predefined structure before it is stored in Azure Cosmos DB. This built-in constraint eliminates the need for additional validation logic.
What should I do if I get this AI-102 question wrong?
Review custom extraction model, then practise related AI-102 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Custom extraction model
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Last reviewed: Jun 24, 2026
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