The correct choice is Azure AI Document Intelligence custom extraction model with a defined schema. This is because custom extraction models allow you to define a precise schema—including field names, data types, and relationships—that the service uses to both extract and validate data from structured and unstructured documents. The built-in schema validation ensures that extracted data conforms to your predefined structure before it reaches Azure Cosmos DB, eliminating the need for separate validation logic. On the AI-102 exam, this scenario tests your understanding of how custom extraction models differ from prebuilt models, which lack schema enforcement. A common trap is to assume you need a separate validation service like Azure Functions or Logic Apps, but the custom model’s schema handles validation natively. Memory tip: think “schema-first extraction”—define the structure, and validation is automatic.
AI-102 Plan and manage an Azure AI solution 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. 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.
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 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 and validate data from both structured and unstructured documents. This built-in schema validation ensures extracted data conforms to your predefined structure before it is stored in Azure Cosmos DB, eliminating the need for additional validation logic.
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.
✗
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
Read the scenario before looking for a memorised answer.
✗
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 here is that candidates often assume prebuilt models (like invoices) are sufficient for schema validation, but they lack the ability to enforce a custom schema across both structured and unstructured documents, which is the core requirement in this question.
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
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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..
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 and validate data from both structured and unstructured documents. This built-in schema validation ensures extracted data conforms to your predefined structure before it is stored in Azure Cosmos DB, eliminating the need for additional validation logic.
What should I do if I get this AI-102 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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