AI-200 Data Management Services And Vector Search Practice Question
You are designing a RAG solution where documents are dynamically updated in Azure AI Search. You notice that search results return outdated information immediately after document updates. What is the cause of this behavior?
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
✓
The indexer has not yet run its scheduled synchronization cycle to process changes from the data source
Azure AI Search indexers run on schedules or require explicit change detection, meaning changes in the source data are not reflected in the search index until the indexer runs or push updates are submitted.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Semantic ranker caches search results for exactly 30 days
Why it's wrong here
Semantic ranker does not cache results for 30 days.
- ✗
Azure OpenAI embedding models require a 24-hour cool-down period before new embeddings become valid
Why it's wrong here
Embedding models generate vectors instantly upon request.
- ✗
Vector indexes in Azure AI Search are read-only after creation and cannot accept updates
Why it's wrong here
Indexes can be updated via push API or indexers; they are not read-only.
- ✓
The indexer has not yet run its scheduled synchronization cycle to process changes from the data source
Why this is correct
Scheduled indexers have a lag time between data source updates and index synchronization.
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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.