AI-200 Data Management Services And Vector Search Practice Question
You are developing a RAG solution and need to evaluate the quality of your vector retrieval results from Azure AI Search. Which metric is commonly used to measure the proportion of relevant documents retrieved in the top-k results?
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
✓
Recall@k
Recall@k measures the proportion of relevant items found in the top-k retrieved results relative to all relevant items.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
HNSW efSearch latency
Why it's wrong here
Latency measures execution time, not retrieval quality.
- ✗
Request Units per second (RU/s)
Why it's wrong here
RU/s measures database throughput, not retrieval relevance quality.
- ✗
BM25 term frequency
Why it's wrong here
BM25 is a keyword scoring formula, not a relevance evaluation metric.
- ✓
Recall@k
Why this is correct
Recall@k measures retrieval effectiveness in finding relevant items among the top-k results.
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.