AI-200 Data Management Services And Vector Search Practice Question
When configuring vector search algorithms in Azure AI Search, you can tune several parameters associated with the Hierarchical Navigable Small World (HNSW) graph. Which THREE parameters are configurable within an HNSW algorithm profile?
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
✓
efConstruction
The HNSW algorithm profile configuration in Azure AI Search accepts parameters such as m (max bi-directional links per node), efConstruction (size of dynamic candidate list during construction), and efSearch (size of dynamic candidate list during search).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
efConstruction
Why this is correct
efConstruction determines the size of the dynamic candidate list during index building, trading off index build time against recall accuracy.
- ✗
bm25K1
Why it's wrong here
bm25K1 is a parameter for the BM25 lexical relevance scoring function, unrelated to HNSW vector graph settings.
- ✗
chunkOverlapSize
Why it's wrong here
chunkOverlapSize is a document chunking parameter used during text splitting, not an HNSW graph tuning parameter.
- ✓
efSearch
Why this is correct
efSearch determines the size of the candidate list during query time, balancing query latency and search recall.
- ✓
m
Why this is correct
The 'm' parameter specifies the number of bi-directional links created for every node during graph construction.
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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
This AI-200 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-200 exam.