AI-200 Data Management Services And Vector Search Practice Question
You are optimizing an enterprise search architecture using Azure AI Search. Which THREE mechanisms help control latency and resource consumption during heavy vector query loads? (Choose three)
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
✓
Tuning HNSW efSearch parameter to balance recall speed and latency
To control latency and resource consumption during heavy vector loads, you can scale out replicas, apply scalar quantization, and tune HNSW efSearch parameters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Tuning HNSW efSearch parameter to balance recall speed and latency
Why this is correct
Lowering efSearch (if slight recall drop is acceptable) reduces query latency.
- ✗
Disabling caching and increasing index document count per partition beyond limits
Why it's wrong here
Overloading partitions degrades performance.
- ✓
Applying scalar quantization to compress vectors and reduce memory overhead
Why this is correct
Quantization reduces memory footprint and speeds up distance calculations.
- ✗
Running exhaustive k-NN searches for every query by default
Why it's wrong here
Exhaustive k-NN increases CPU usage and latency.
- ✓
Scaling out search replicas to distribute query traffic across nodes
Why this is correct
Adding replicas increases query throughput capacity and reduces per-node load.
About these practice questions
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JA
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.