AI-200 Data Management Services And Vector Search Practice Question
Which THREE features are common to both Azure AI Search and Azure Cosmos DB for NoSQL when performing vector search?
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
✓
Ability to filter results based on non-vector document properties.
Both services support HNSW-based indexing, allow for filtering metadata alongside vector searches, and provide similarity search functions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ability to filter results based on non-vector document properties.
Why this is correct
Both services allow for pre-filtering or post-filtering based on metadata.
- ✗
Support for semantic re-ranking (Semantic Ranker).
Why it's wrong here
Semantic Ranker is a feature specific to Azure AI Search.
- ✓
Support for HNSW indexing algorithms.
Why this is correct
HNSW is a standard algorithm implemented by both services for vector search.
- ✗
Automatic translation of SQL to Vector queries.
Why it's wrong here
Neither service offers automatic translation of standard SQL into vector-based similarity queries.
- ✓
Similarity search based on vector distance metrics.
Why this is correct
Similarity calculation is the core function of both vector search implementations.
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.