AI-200 Data Management Services And Vector Search Practice Question
Your team is implementing hybrid search in Azure AI Search combining full-text keyword search and vector search. Users report that certain exact product serial numbers are missing from the semantic ranking results. Which query parameter should you adjust to balance the influence of BM25 text search scores against vector cosine similarity scores?
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
✓
Enabling semantic ranking and providing a semanticConfiguration with targeted weight overrides.
When performing hybrid queries in Azure AI Search, the search engine uses Reciprocal Rank Fusion (RRF) to combine results from text and vector searches. You can influence the weighting or use standard RRF parameters, but adjusting semantic ranking configurations or ensuring proper semantic configuration boosts can address relevance blending. Specifically, adjusting query parameters like semantic configuration or custom scoring profiles allows fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increasing the efSearch parameter in the index definition.
Why it's wrong here
efSearch controls the number of nearest neighbors examined during HNSW graph traversal at query time, affecting recall depth rather than BM25-to-vector score balancing.
- ✓
Enabling semantic ranking and providing a semanticConfiguration with targeted weight overrides.
Why this is correct
Adding a semantic configuration with designated title, content, and keyword fields ensures that exact alphanumeric serial numbers are correctly captured via secondary lexical boosting and reranked properly.
- ✗
Adjusting the top parameter to retrieve a larger candidate pool before RRF combination.
Why it's wrong here
Retrieving a larger top candidate set helps coverage, but does not directly control the scoring weight distribution between BM25 and vector distance.
- ✗
Switching the vector similarity metric from cosine to euclidean distance.
Why it's wrong here
Changing the distance metric changes how vectors are compared mathematically, but does not resolve lexical keyword matching failures for serial numbers.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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