AI-102 Plan and manage an Azure AI solution Practice Question
Your organization is using Azure AI Search with semantic ranking. Users report that search results are not showing relevant documents at the top. You need to improve relevance. What should you configure?
⚠ Common exam trap
Microsoft often tests the misconception that enabling semantic ranking is automatic with the service tier, but candidates must explicitly configure a semantic configuration on the index and specify it in the query request to activate the feature.
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 configuration on the index
Semantic search configuration is required to enable semantic ranking, which uses deep learning models to re-rank search results based on contextual relevance rather than just keyword matching. Without this configuration, the index cannot leverage semantic ranking even if the service tier supports it, so enabling it directly addresses the user's complaint about irrelevant documents appearing at the top.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add synonyms to the index
Why it's wrong here
Synonyms only expand query terms to matching variants; they cannot reorder results by semantic relevance. Semantic ranking re-scores the top L1 results using a language model, so the fix lies in enabling or tuning that ranker, not the synonym map. Synonyms suit vocabulary mismatch, such as users typing "laptop" for indexed "notebook".
- ✗
Define a custom scoring profile
Why it's wrong here
Scoring profiles adjust BM25 field weights and functions before semantic ranking runs, so they cannot correct the semantic re-ranker's ordering. The reported symptom points to semantic ranking configuration itself. Scoring profiles are correct when boosting by freshness, magnitude, or specific fields in keyword relevance.
- ✓
Enable semantic search configuration on the index
Why this is correct
Semantic ranking only reorders results when a semantic configuration is defined on the index and referenced in the query. Without it, scoring falls back to BM25 keyword relevance, so enabling the semantic configuration is what lifts relevant documents to the top.
- ✗
Change the index analyzer to a different language analyzer
Why it's wrong here
Language analysers affect lexical tokenisation at index and query time, not the semantic re-ranker's L2 ordering. Relevance failures under semantic ranking stem from that ranker's configuration. Swapping analysers is correct when stemming, plurals, or compound words in a specific language break keyword matching.
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