AI-102 Plan and manage an Azure AI solution Practice Question
Your Azure AI Search index stores customer support tickets. You need to implement a search feature that returns semantically similar results even if the query uses different wording. Which configuration should you enable?
⚠ Common exam trap
Candidates often confuse synonym maps (which handle predefined word equivalence) with semantic search (which handles contextual meaning), leading them to choose synonym maps when the question explicitly requires handling of different wording beyond simple synonyms.
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
✓
Enable semantic search and configure a semantic configuration
Semantic search in Azure AI Search uses deep neural networks to understand the intent and context of a query, returning results that are semantically similar even when the wording differs. By enabling semantic search and configuring a semantic configuration, you define which fields are used for summarization and ranking, which directly addresses the requirement for meaning-based matching rather than keyword matching.
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 simple query parsing with searchMode=any
Why it's wrong here
searchMode=any broadens matching to documents containing any query term, but it operates on lexical tokens, not meaning, so paraphrased queries still fail. It suits recall-oriented keyword searches where partial term overlap is acceptable, not semantic similarity.
- ✗
Add a synonym map with custom entries
Why it's wrong here
Synonym maps only expand explicitly listed term pairs, so they cannot match paraphrases absent from the map. They are correct when query vocabulary is known and finite, such as mapping product codes to names, not for open-ended semantic similarity.
- ✓
Enable semantic search and configure a semantic configuration
Why this is correct
Semantic search applies a reranking model over results, using a semantic configuration that maps title, content and keyword fields. This lets queries match by meaning rather than exact terms, satisfying the requirement for semantically similar results despite different wording.
- ✗
Enable fuzzy search on the index
Why it's wrong here
Fuzzy search corrects typos and edit-distance variations in individual terms; it does not encode meaning, so differently worded queries return nothing. It is the right choice for handling misspellings and minor character errors, not semantic matching.
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