The correct combination is Azure AI Document Intelligence and Custom Entity Extraction. Document Intelligence handles the heavy lifting of extracting raw text from diverse legal document formats, including PDFs, Word files, and scanned images, by using its optical character recognition and layout analysis capabilities. Custom Entity Extraction, a feature of Azure AI Language, then allows you to define and train models to identify specific clauses, party names, dates, and monetary amounts from that extracted text, making it ideal for the varying structures found in legal documents. On the AI-102 exam, this scenario tests your ability to distinguish between Azure AI services that process documents versus those that analyze language, with a common trap being to choose Computer Vision alone—which can read text but cannot extract custom entities without additional training. Remember the memory tip: “Read it with Document Intelligence, extract it with Language.”
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing 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.
Your organization has a large corpus of legal documents that need to be analyzed for specific clauses. You need to extract key information such as party names, dates, and monetary amounts. The solution must be able to handle varying document formats (PDF, Word, scanned images). 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 and Custom Entity Extraction
Option D is correct because Document Intelligence (formerly Form Recognizer) extracts text from documents (including scanned images), and Custom Entity Extraction in Azure AI Language extracts the needed entities. Option A is wrong because Translator is for translation, not extraction. Option B is wrong because Computer Vision can read text but does not extract custom entities. Option C is wrong because Speech is for audio, not documents.
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 and Custom Entity Extraction
Why this is correct
Document Intelligence extracts text from various formats; Custom Entity Extraction identifies specific entities.
Related concept
Static NAT maps one inside address to one outside address.
✗
Azure AI Computer Vision and Custom Entity Extraction
Why it's wrong here
Computer Vision provides OCR but not full document parsing; Document Intelligence is better suited.
✗
Azure AI Translator and Custom Entity Extraction
Why it's wrong here
Translator is not needed; entity extraction alone doesn't process documents.
✗
Azure AI Speech and Custom Entity Extraction
Why it's wrong here
Speech is for audio, not text documents.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 natural language processing solutions — This question tests Implement natural language processing 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 and Custom Entity Extraction — Option D is correct because Document Intelligence (formerly Form Recognizer) extracts text from documents (including scanned images), and Custom Entity Extraction in Azure AI Language extracts the needed entities. Option A is wrong because Translator is for translation, not extraction. Option B is wrong because Computer Vision can read text but does not extract custom entities. Option C is wrong because Speech is for audio, not documents.
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.
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Question Discussion
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