The correct answer is to use Azure AI Language entity extraction as a custom skill and enable fuzzy search in the index. This combination directly addresses the requirement to extract attributes like color, size, and brand from product descriptions while handling misspelled queries like "red runing shoes" through fuzzy matching, which tolerates minor typos by finding terms within a specified edit distance. On the AI-102 exam, this scenario tests your understanding of how to build a custom enrichment pipeline in Azure AI Search, where a custom skill calls the Language service for entity recognition, and the index’s search profile is configured with a fuzzy search parameter. A common trap is confusing Azure AI Document Intelligence or OCR for text extraction from a database, but those services are designed for documents and images, not structured text from Cosmos DB. Remember the memory tip: “Entities for extraction, fuzzy for correction” — the Language service pulls out the attributes, and fuzzy search fixes the spelling.
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.
You are a developer at an e-commerce company. The company wants to build a product search feature that allows customers to search for products using natural language phrases like "red running shoes under $100". The product catalog is stored in Azure Cosmos DB and includes product descriptions, prices, and categories. The solution must use Azure AI Search and must extract entities from product descriptions to enable filtering (e.g., color, size, brand). The search must also support fuzzy matching for misspelled queries. You need to design the indexing pipeline. Which actions should you take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index
Option C is correct. Using Azure AI Language for entity extraction extracts attributes like color and brand, and enabling fuzzy search in the index handles misspellings. Option A is incorrect because Azure AI Document Intelligence is for document extraction, not text from a database. Option B is incorrect because OCR is for images. Option D is incorrect because the Language service does not perform vector search.
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.
✗
Use Azure AI Language key phrase extraction, and enable vector search
Why it's wrong here
Key phrase extraction does not extract structured attributes; vector search is not needed for fuzzy matching.
✗
Use Azure AI Document Intelligence to extract entities, and enable semantic ranking
Why it's wrong here
Document Intelligence is not needed for text already in Cosmos DB.
✓
Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index
Static NAT maps one inside address to one outside address.
✗
Use Azure AI Vision OCR to extract text, and enable synonyms
Why it's wrong here
OCR is for images, not for text in a database.
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.
Trap categories for this question
Keyword trap
Key phrase extraction does not extract structured attributes; vector search is not needed for fuzzy matching.
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: Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index — Option C is correct. Using Azure AI Language for entity extraction extracts attributes like color and brand, and enabling fuzzy search in the index handles misspellings. Option A is incorrect because Azure AI Document Intelligence is for document extraction, not text from a database. Option B is incorrect because OCR is for images. Option D is incorrect because the Language service does not perform vector search.
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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