AI-200 Data Management Services And Vector Search Practice Question
You are troubleshooting a slow vector query performance issue in Azure Cosmos DB for NoSQL. Which THREE actions can you take to improve vector search execution times? (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
✓
Use the diskANN indexing type instead of flat indexes for large collections
Improving vector search performance in Cosmos DB involves selecting efficient index types like diskANN, tuning query parameters, and ensuring proper logical partitioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the diskANN indexing type instead of flat indexes for large collections
Why this is correct
diskANN provides fast approximate nearest neighbor search compared to scanning flat indexes.
- ✓
Ensure your container is partitioned effectively to distribute query load
Why this is correct
Effective partitioning prevents hot spotting and distributes query execution across physical partitions.
- ✗
Convert all vector embeddings to base64 string format
Why it's wrong here
Base64 strings prevent vector distance indexing and functions from executing.
- ✗
Disable all non-vector indexing policies on the container
Why it's wrong here
Disabling indexing entirely would hurt filtering performance and require full container scans.
- ✓
Include filter conditions in your query to narrow the search scope before vector distance ranking
Why this is correct
Filtering data before ranking reduces the computational overhead of vector distance calculations.
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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
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