- A
Use the Read API to extract all text and then use a custom regex to parse fields.
Why wrong: Regex is fragile for varying layouts.
- B
Use the Azure AI Document Intelligence prebuilt invoice model.
Prebuilt invoice model handles varying layouts and mixed text.
- C
Train a Custom Vision object detection model to locate fields.
Why wrong: Custom Vision is not optimized for document field extraction.
- D
Label hundreds of invoices and train a custom Azure AI Document Intelligence model.
Why wrong: Requires extensive labeling; prebuilt model is better.
Question 32 of 993
AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision solutions. 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 designing a solution that uses Azure AI Vision to extract text from scanned invoices. The invoices vary in layout and include both printed and handwritten fields. The solution must achieve high accuracy with minimal manual labeling. Which approach should you recommend?
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
✓
Use the Azure AI Document Intelligence prebuilt invoice model.
The Azure AI Document Intelligence prebuilt invoice model is specifically designed to extract common fields from invoices with high accuracy, handling both printed and handwritten text without requiring manual labeling. It uses advanced OCR and deep learning models trained on thousands of invoices, making it ideal for varied layouts and minimal setup.
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.
- ✗
Use the Read API to extract all text and then use a custom regex to parse fields.
Why it's wrong here
Regex is fragile for varying layouts.
- ✓
Use the Azure AI Document Intelligence prebuilt invoice model.
Why this is correct
Prebuilt invoice model handles varying layouts and mixed text.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Train a Custom Vision object detection model to locate fields.
Why it's wrong here
Custom Vision is not optimized for document field extraction.
- ✗
Label hundreds of invoices and train a custom Azure AI Document Intelligence model.
Why it's wrong here
Requires extensive labeling; prebuilt model is better.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often overestimate the need for custom training or regex-based parsing, not realizing that Azure provides a prebuilt, high-accuracy model specifically for invoices that requires zero manual labeling.
Detailed technical explanation
How to think about this question
The prebuilt invoice model leverages Azure AI Document Intelligence's layout analysis and key-value pair extraction, combining OCR with transformer-based models to identify fields like invoice ID, date, total, and line items even when layouts vary. Under the hood, it uses a combination of spatial attention and semantic parsing to map text to field labels, achieving high accuracy without custom training. In real-world scenarios, this model handles mixed printed and handwritten text by using the Read API for OCR and then applying a domain-specific neural network to extract structured data.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the Azure AI Document Intelligence prebuilt invoice model. — The Azure AI Document Intelligence prebuilt invoice model is specifically designed to extract common fields from invoices with high accuracy, handling both printed and handwritten text without requiring manual labeling. It uses advanced OCR and deep learning models trained on thousands of invoices, making it ideal for varied layouts and minimal setup.
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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Last reviewed: Jul 4, 2026
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