Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are building an Azure AI Search solution that indexes data from multiple sources, including SQL Database and Azure Blob Storage. The index must be updated within 15 minutes of any source change. Which approach should you use to achieve near-real-time indexing?
⚠ Common exam trap
Microsoft often tests the misconception that indexer schedules can achieve near-real-time indexing, but the trap is that indexers have inherent polling intervals and processing overhead that prevent sub-minute latency, making the push API the only viable option for true near-real-time updates.
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 push API to send updates as soon as data changes
The push API (Azure Cognitive Search REST API or SDK) allows you to directly upload documents to the index as soon as data changes occur, bypassing the indexer's polling cycle. This provides sub-minute latency, meeting the 15-minute near-real-time requirement. Indexers with schedules or enrichment pipelines introduce inherent delays and are not designed for sub-minute 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.
- ✗
Enable incremental enrichment on the skillset
Why it's wrong here
Incremental enrichment is for re-processing only enriched documents, not for data updates.
- ✓
Use the push API to send updates as soon as data changes
Why this is correct
The push API allows you to add or update documents in the index in real-time.
- ✗
Use an indexer with a schedule set to run every 5 minutes
Why it's wrong here
Indexers have a minimum interval of 5 minutes, but changes may not be detected immediately depending on change tracking.
- ✗
Enable semantic search to speed up indexing
Why it's wrong here
Semantic search affects query time, not indexing speed.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 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-102 exam.