AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Which TWO features of Azure AI Search allow you to improve the relevance of search results for users?
⚠ Common exam trap
It's easy for candidates to confuse features that expand recall (synonym maps) or improve user experience (suggesters) with features that directly improve relevance ranking, leading them to select A or C instead of the correct scoring profiles and semantic search.
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
✓
Semantic search
Semantic search (B) is correct because it uses Microsoft's language understanding models to rerank results with semantic captions and answers, boosting relevance beyond keyword matching. Scoring profiles (D) are correct because they let you define custom ranking functions (e.g., boosting by freshness, magnitude, or tags) that directly influence the relevance score of returned documents. Synonym maps (A) expand queries with equivalent terms but only broaden matching, not rank relevance. Suggesters (C) enable type-ahead autocomplete, which improves query input rather than result relevance. Filterable fields (E) restrict the result set with OData filters but do not affect relevance ranking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Synonym maps
Why it's wrong here
Synonym maps expand a query term to equivalent terms, increasing recall, but they do not change how matched documents are ranked or scored. They are correct when users employ domain jargon or abbreviations needing equivalence, whereas relevance ordering is controlled by scoring profiles and field weights.
- ✓
Semantic search
Why this is correct
Semantic search applies Microsoft's language understanding models to rerank results, promoting passages that are semantically relevant rather than only keyword-matched. This directly improves relevance ranking for users, satisfying the stem's requirement to enhance search result relevance.
- ✗
Suggesters
Why it's wrong here
Suggesters power type-ahead autocomplete and query suggestions as the user types; they do not rescore or reorder matched documents. They are the right feature when building a search box with predictive completion, but relevance improvement comes from scoring profiles, boosting, and synonym expansion instead.
- ✓
Scoring profiles
Why this is correct
Scoring profiles apply weighted boosts to specific index fields and functions at query time, so documents matching business-priority criteria rank higher. This customises relevance ranking per user needs, satisfying the stem's requirement to improve search result relevance.
- ✗
Filterable fields
Why it's wrong here
Filterable fields restrict which documents match a query; they narrow the result set rather than reorder it, so they do not affect relevance scoring. They are genuinely correct when a scenario requires exact attribute constraints, such as returning only in-stock items, but relevance ranking is governed by scoring profiles and field weights.
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