AI-200 Data Management Services And Vector Search Practice Question
You are designing a data layer architecture in Azure Cosmos DB for NoSQL for an AI application that performs high-frequency writes and real-time vector searches. You need to understand how vector indexing impacts write operations. What is the primary trade-off?
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
✓
Writing documents consumes more Request Units (RUs) because vector indexes must be updated
Indexing vector embeddings requires maintaining complex index structures (like diskANN graphs), which increases RU (Request Unit) consumption during document inserts and updates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vector properties cannot be updated after initial document creation
Why it's wrong here
Documents and their vector properties can be freely updated or replaced.
- ✗
Writes are completely blocked until the vector index reaches 100% synchronization
Why it's wrong here
Cosmos DB indexes updates asynchronously or incrementally; writes are not blocked.
- ✓
Writing documents consumes more Request Units (RUs) because vector indexes must be updated
Why this is correct
Maintaining vector indexes introduces additional computational overhead during write operations, increasing RU costs.
- ✗
Writes require explicit distributed transactions across regions
Why it's wrong here
Writes operate locally on the write region and replicate asynchronously without forcing distributed transaction locks for vector updates.
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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.