AI-200 Data Management Services And Vector Search Practice Question
You are migrating a large vector dataset into an Azure AI Search index. To maximize ingestion speed and throughput, you temporarily adjust index settings. Which index configuration property should you modify during bulk ingestion?
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
✓
Scale out search units (replicas and partitions) to distribute ingestion load
During heavy bulk ingestion, increasing the replica and partition count or adjusting merge/indexing batch sizes improves speed, but specifically, scaling out search units or temporarily disabling semantic ranker overhead optimizes throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set HNSW efConstruction to 10,000 to maximize graph build precision
Why it's wrong here
Increasing efConstruction to an extremely high value slows down ingestion significantly.
- ✓
Scale out search units (replicas and partitions) to distribute ingestion load
Why this is correct
Scaling out search units increases indexing throughput for massive bulk uploads.
- ✗
Disable all scoring profiles and set vector dimensions to 1
Why it's wrong here
Changing vector dimensions would corrupt the embeddings of 1536-dimensional models.
- ✗
Switch the index tier from Standard to Free
Why it's wrong here
The Free tier has strict limits and would cause bulk ingestion to fail.
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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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