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AI-102 Entity extraction Practice Question
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 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 Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index
Using Azure AI Language entity extraction as a custom skill extracts attributes like color, size, and brand from product descriptions, and enabling fuzzy search in the index handles misspellings. Option A is incorrect because key phrase extraction identifies topics, not specific entities like color or brand, and vector search is for similarity matching, not fuzzy matching for typos. Option B is incorrect because Azure AI Document Intelligence is designed to extract text from documents (e.g., PDFs, images), not to extract named entities from text already stored in a database; semantic ranking improves relevance but does not perform entity extraction. Option D is incorrect because Azure AI Vision OCR extracts text from images, not from product descriptions in Cosmos DB, and synonyms expand queries but do not extract entities needed for filtering.
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
Azure AI Language key phrase extraction does not extract structured entities like color, size, or brand. Vector search is for semantic similarity, not fuzzy matching for misspellings.
- ✗
Use Azure AI Document Intelligence to extract entities, and enable semantic ranking
Why it's wrong here
Azure AI Document Intelligence is designed for extracting text and structure from documents, not for entity extraction from product descriptions in a database. Semantic ranking improves relevance but does not handle entity-based filtering or fuzzy matching.
- ✓
Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index
Why this is correct
Azure AI Language entity extraction as a custom skill can extract attributes like color and brand from product descriptions. Fuzzy search in the index handles misspelled queries.
- ✗
Use Azure AI Vision OCR to extract text, and enable synonyms
Why it's wrong here
Azure AI Vision OCR is used for extracting text from images, not for extracting entities from structured text. Synonyms may help with related terms but do not address fuzzy matching for misspellings.
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