AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Your team is using Azure AI Search to index a large collection of technical manuals. Users report that searches for 'disk failure' do not return relevant results because the manuals use terms like 'hard drive crash'. Which feature should you implement to improve recall?
⚠ Common exam trap
Many candidates confuse semantic search (which improves ranking via language models) with synonym expansion (which directly addresses vocabulary mismatch by broadening the query), leading them to choose option C instead of D.
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
✓
Add a synonym map to the index
A synonym map in Azure AI Search allows you to define equivalent terms (e.g., 'disk failure' = 'hard drive crash') so that queries automatically expand to include synonyms. This directly addresses the vocabulary mismatch between user queries and indexed content, improving recall without requiring changes to the documents or queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply a filter
Why it's wrong here
Filters apply exact boolean comparisons on fields, so 'disk failure' still matches nothing when the indexed text says 'hard drive crash'; they narrow result sets rather than expanding term matches. Filters are tempting because they refine queries, and they would be correct when restricting results by a known field value such as document type.
- ✗
Configure a scoring profile
Why it's wrong here
Scoring profiles adjust the ranking of documents already matched by the query; they cannot retrieve documents containing none of the query terms, so recall stays unchanged. They are tempting because they tune relevance, and they would be correct when relevant matches exist but appear too low in the result order.
- ✗
Enable semantic search
Why it's wrong here
Semantic search reranks an existing result set using language models; it does not map 'disk failure' to 'hard drive crash', so documents lacking those terms are never retrieved. It is tempting because it improves relevance, and it would be correct when matched results need better ordering rather than broader matching.
- ✓
Add a synonym map to the index
Why this is correct
A synonym map expands queries so 'disk failure' also matches 'hard drive crash', raising recall for terminology mismatches. It applies at query time against the index, directly addressing the vocabulary gap between user phrasing and manual wording.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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