The correct combination is Azure AI Document Intelligence custom classification and extraction models. This is because scanned invoices require both vendor-level categorization and structured field parsing, which the pre-built invoice model cannot handle—it lacks custom vendor classification. A custom classification model assigns each invoice to a specific vendor class, while a custom extraction model then pulls key fields like total amount, date, and invoice number from the classified document. On the AI-102 exam, this scenario tests your understanding of when to move beyond pre-built models to custom solutions, a common trap being the outdated “Form Recognizer” name or assuming OCR alone suffices for structured extraction. Remember the two-step rule: classify first, then extract—think “Classify the vendor, extract the fields.”
AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 extracts information from scanned invoices. The solution must automatically classify invoices by vendor and extract key fields (total amount, date, invoice number). Which combination of Azure AI services should you use?
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 classification and extraction models
The correct answer is to use Azure AI Document Intelligence with a custom classification model for vendor classification and a custom extraction model for field extraction. Option A is wrong because pre-built invoice model does not support custom vendor classification. Option B is wrong because Azure AI Vision OCR alone cannot extract structured fields. Option D is wrong because Form Recognizer is the older name and the service is now Document Intelligence; the combination is correct but the naming is outdated, and it does not include classification.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 AI Document Intelligence custom classification and extraction models
Why this is correct
Custom models allow classification by vendor and extraction of specific fields tailored to each vendor's invoice layout.
Related concept
Static NAT maps one inside address to one outside address.
✗
Azure AI Vision OCR and Azure AI Language
Why it's wrong here
OCR provides raw text, but Language service is for NLP, not document structure extraction.
✗
Azure AI Form Recognizer (deprecated) with custom models
Why it's wrong here
Form Recognizer is deprecated; the correct service is Document Intelligence. Also, it lacks built-in classification.
✗
Azure AI Document Intelligence pre-built invoice model
Why it's wrong here
Pre-built invoice model extracts common fields but does not provide vendor-specific classification.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
Implement computer vision solutions — This question tests Implement computer vision solutions — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Azure AI Document Intelligence custom classification and extraction models — The correct answer is to use Azure AI Document Intelligence with a custom classification model for vendor classification and a custom extraction model for field extraction. Option A is wrong because pre-built invoice model does not support custom vendor classification. Option B is wrong because Azure AI Vision OCR alone cannot extract structured fields. Option D is wrong because Form Recognizer is the older name and the service is now Document Intelligence; the combination is correct but the naming is outdated, and it does not include classification.
What should I do if I get this AI-102 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company wants to extract key-value pairs from scanned invoices using Azure AI. Which service should they use?
easy
A.Read API
B.Custom Vision
C.OCR API
✓ D.Azure AI Document Intelligence
Why D: Option A is correct because Azure AI Document Intelligence (formerly Form Recognizer) is designed to extract key-value pairs from documents. Option B is wrong because Custom Vision is for image classification. Option C is wrong because OCR only extracts text without structure. Option D is wrong because the Read API extracts text but not key-value pairs.
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Question Discussion
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