How should I use these Information Extraction practice questions?

Read each scenario carefully and choose your answer before revealing the explanation. Then check why your choice was right or wrong. Repeat until the reasoning feels automatic.

Can I practise just Information Extraction questions in a focused session?

Yes — use the session launcher on this page to start a 10-, 20-, 30- or 50-question session drawn entirely from the Information Extraction domain.

AI-103 · topic practice

Information Extraction practice questions

Practise Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103) (AI-103) Information Extraction practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security

20 questionsDomain: Information Extraction

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What the exam tests

What to know about Information Extraction

Information Extraction questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Information Extraction exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Information Extraction questions

20 questions · select your answer, then reveal the explanation

Question 1mediummultiple choice

Read the full Information Extraction explanation →

You are developing a custom extraction model using Azure AI Document Intelligence Studio. Your training dataset consists of multi-page invoices with varying structures, and some fields appear only on the final page. How should you structure your tagging configuration?

Trap 1: Create a separate custom model for the final page and chain them…

An orchestration model is not needed simply because a field appears on a later page of a multi-page document.

Trap 2: Split every multi-page invoice into individual single-page…

Splitting destroys document boundary context and is unnecessary for multi-page document support.

Trap 3: Define all late-appearing fields as optional metadata parameters in…

Fields are tagged visually in the Studio or defined in the layout schema, not as metadata parameters.

Study all Information Extraction common traps →

A
Create a separate custom model for the final page and chain them using an orchestration model.
Why wrong: An orchestration model is not needed simply because a field appears on a later page of a multi-page document.
B
Split every multi-page invoice into individual single-page documents before uploading.
Why wrong: Splitting destroys document boundary context and is unnecessary for multi-page document support.
C
Define all late-appearing fields as optional metadata parameters in the training configuration JSON file.
Why wrong: Fields are tagged visually in the Studio or defined in the layout schema, not as metadata parameters.
D
Tag the fields on whatever page they appear across the sample documents in Document Intelligence Studio.
Document Intelligence custom models support multi-page documents natively, allowing you to tag fields wherever they appear.

Information Extraction practice questions

What to know about Information Extraction

Common Information Extraction exam traps

Information Extraction questions

You are developing a custom extraction model using Azure AI Document Intelligence Studio. Your training dataset consists of multi-page invoices with varying structures, and some fields appear only on the final page. How should you structure your tagging configuration?

You are troubleshooting an Azure AI Document Intelligence custom extraction model. When you test a new document, certain currency values are being extracted with the currency symbol missing. Where should you look in the JSON response structure to find the normalized value?

Your team needs to extract data from a large volume of standardized tax forms using Azure AI Document Intelligence. The forms have a fixed layout where fields are always in the exact same location. Which model type provides the fastest training and highest accuracy for this specific use case?

You are configuring an Azure AI Document Intelligence prebuilt model to extract key-value pairs from standard invoices. Which API operation should your application call to process the document asynchronously?

You are integrating Azure AI Document Intelligence into a web application. You call the analyzeDocument API and receive a 202 Accepted response. What is the correct pattern for your application to retrieve the final extraction results?

Which prebuilt Azure AI Document Intelligence model should you use to extract line items, merchant name, transaction date, and total tax from point-of-sale purchase receipts?

You are using Azure AI Document Intelligence to extract information from identity documents, such as passports and driver licenses. Which prebuilt model should you select?

Your company processes business cards from international clients. Which prebuilt Azure AI Document Intelligence model should you use to extract names, company names, emails, and phone numbers from these cards?

You are configuring a custom extraction model in Azure AI Document Intelligence and want to compose multiple specialized models into a single endpoint that automatically classifies and routes documents. Which REST API endpoint should you use to create this composed model?

You are implementing cross-domain document extraction where incoming PDFs contain complex nested tables with merged cells. You need to extract these tables accurately into structured JSON. Which capability of Azure AI Document Intelligence should you rely on?

You are training a custom template model in Azure AI Document Intelligence and notice that one of your key fields is consistently failing to extract on documents where the label text is vertically oriented. How should you address this issue?

You are auditing an Azure AI Document Intelligence solution for data privacy compliance. Documents contain sensitive Personally Identifiable Information (PII). When using prebuilt models or custom models, how is data handled by Microsoft regarding model training?

You are building an automated invoice processing pipeline using Azure AI Document Intelligence. Some incoming invoices contain handwritten vendor signatures and handwritten approval dates. Which feature must you ensure is supported and enabled when configuring your analysis request?

You are configuring an Azure AI Document Intelligence container for deployment in an on-premises disconnected environment. Which environment variable is mandatory for the container to start successfully and report billing usage?

Which TWO prebuilt models are officially available in Azure AI Document Intelligence for extracting specialized domain data? (Choose TWO)

When building a custom template model in Azure AI Document Intelligence Studio, which THREE types of data or attributes can you extract by labeling your training documents? (Choose THREE)

Which TWO file formats are natively supported as inputs for analysis in Azure AI Document Intelligence? (Choose TWO)

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