AI-200 Data Management Services And Vector Search Practice Question
When configuring an Azure AI Search vector index, which TWO parameters can be adjusted within the HNSW algorithm configuration to trade off between search speed, memory usage, and recall accuracy? (Choose two)
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
✓
efSearch
In HNSW, parameters like m (links per node) and efSearch (size of candidate list during search) allow tuning between recall accuracy and query performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
efSearch
Why this is correct
efSearch controls the size of the candidate list during the search phase, affecting recall and latency.
- ✗
replicaCount
Why it's wrong here
replicaCount governs search availability and throughput scaling, not HNSW graph tuning.
- ✗
partitionCount
Why it's wrong here
partitionCount is for data partitioning across storage partitions.
- ✗
maxTextLength
Why it's wrong here
maxTextLength is unrelated to HNSW algorithm tuning.
- ✓
m
Why this is correct
m controls the number of bidirectional links per node, affecting index build time, memory, and recall.
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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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