The correct combination is Azure AI Document Intelligence’s custom extraction model paired with Azure AI Language’s custom text classification. This works because Document Intelligence allows you to train a model on domain-specific research papers to extract fields like author names, publication dates, and references, while Azure AI Language’s custom text classification enables you to categorize papers into topics using your own labeled data. On the AI-102 exam, this scenario tests your understanding of how to combine Azure AI services for a knowledge mining pipeline, specifically distinguishing between prebuilt and custom capabilities. A common trap is choosing Azure AI Search with built-in OCR skills, which lacks the custom extraction and classification needed for domain-specific documents. Remember the pairing: Document Intelligence handles the “what” (extraction), and Language handles the “where” (classification)—think “Extract then Classify” as your memory anchor.
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
This AI-102 practice question tests your understanding of implement knowledge mining and information extraction 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 team is building a knowledge mining solution for research papers. You need to automatically categorize papers into topics and extract author names, publication dates, and references. The solution must use custom models because the papers are domain-specific. Which combination of Azure 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's custom extraction model and Azure AI Language's custom text classification
Option C is correct because Azure AI Document Intelligence can extract custom fields, and Azure AI Language can be used for custom classification. Option A lacks custom extraction; Option B lacks classification; Option D uses OCR skill which is not for custom models.
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's pre-built invoice model and Azure Bot Service
Why it's wrong here
Invoice model is not for research papers; Bot Service is not needed.
✓
Azure AI Document Intelligence's custom extraction model and Azure AI Language's custom text classification
Why this is correct
Custom models can handle domain-specific extraction and classification.
Related concept
Static NAT maps one inside address to one outside address.
✗
Azure AI Search's built-in OCR skill and a custom skill using Azure Functions
Why it's wrong here
OCR extracts text but not structured fields; custom skill is possible but not the best combination.
✗
Azure AI Language's pre-built entity extraction and Azure AI Search
Why it's wrong here
Pre-built models may not capture domain-specific entities.
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 knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction 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's custom extraction model and Azure AI Language's custom text classification — Option C is correct because Azure AI Document Intelligence can extract custom fields, and Azure AI Language can be used for custom classification. Option A lacks custom extraction; Option B lacks classification; Option D uses OCR skill which is not for custom models.
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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