20+ practice questions focused on Information Extraction — one of the most tested topics on the Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103) (AI-103) exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Information Extraction PracticeYou 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?
Explanation: In Document Intelligence's analyze response, extracted fields are organized under the documents array, and each field has a typed value object. For a currency field, the normalized value (amount plus currency code) is exposed under valueCurrency, which includes amount and currencyCode properties. The raw text with the symbol may appear in the field's content, but the normalized representation lives in valueCurrency.
You are designing a high-throughput document processing pipeline using Azure AI Document Intelligence. To optimize latency and cost, you want to extract only the text and layout structures (paragraphs, lines, words, and tables) from a multi-page PDF without performing key-value extraction or using custom models. Which model ID should you specify in your API request?
Explanation: prebuilt-layout extracts text plus layout structures — paragraphs, lines, words, selection marks, and tables — without key-value pairs or custom training. It is the correct model when you need structural document understanding at scale. prebuilt-read returns only raw text, and prebuilt-document adds key-value extraction, which the scenario explicitly excludes.
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?
Explanation: The compose operation is exposed as POST /documentintelligence/documentModels:compose?api-version=2024-02-29-preview, which takes a list of existing custom model IDs and a new modelId, and returns a composed model that can classify and route documents to the appropriate submodel. This is the documented endpoint for building composed models.
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?
Explanation: Azure AI Document Intelligence's built-in OCR (Read model) natively detects both printed and handwritten text across its supported prebuilt and custom models, so no special parameter or separate service is required. Handwriting recognition is a core capability of the underlying Read/OCR engine, meaning the invoice model can extract handwritten signatures and approval dates without additional configuration beyond ensuring the model supports it. This makes option A the correct answer because it reflects the actual architecture of the service.
Which TWO prebuilt models are officially available in Azure AI Document Intelligence for extracting specialized domain data? (Choose TWO)
Explanation: Option C, prebuilt-tax.us.w2, is correct because Azure AI Document Intelligence ships a dedicated US W-2 tax form model that extracts fields such as wages, tips, and federal income tax withheld from IRS W-2 documents. Option E, prebuilt-invoice, is correct because the prebuilt invoice model is an officially supported specialized-domain model that extracts key invoice fields like vendor name, invoice date, due date, and line items. The other options are not official prebuilt model IDs: prebuilt-socialSecurityCard, prebuilt-architecturalDrawing, and prebuilt-medicalChart do not exist in the Document Intelligence prebuilt model catalog, so they cannot be selected.
+15 more Information Extraction questions available
Practice all Information Extraction questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Information Extraction. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Information Extraction questions on the AI-103 frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Information Extraction is tested as part of the Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103) (AI-103) blueprint. Practicing with targeted Information Extraction questions ensures you can handle any format or difficulty that appears.
Yes. Courseiva provides free AI-103 practice questions across all exam topics and domains. The platform includes topic-based practice, mock exams, missed-question review, bookmarked questions, and readiness tracking — no account required.
Difficulty is subjective, but Information Extraction is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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