Implement knowledge mining and information extraction solutions →hardMultiple SelectObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Which TWO configurations are required to enable incremental enrichment in Azure AI Search?
⚠ Common exam trap
Many exam-takers confuse enabling blob metadata extraction (Option B) with the caching mechanism required for incremental enrichment, but metadata extraction alone does not create or manage the enrichment cache.
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
✓
Define a projection in the skillset to store enriched data.
Defining a projection in the skillset is required to store enriched data in a knowledge store, which is a prerequisite for incremental enrichment. Incremental enrichment relies on caching enriched content so that only changed documents are reprocessed, and projections define how that enriched data is stored in Azure Storage for later reuse.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the indexer to run in 'once' mode.
Why it's wrong here
Incremental enrichment works with scheduled runs.
- ✗
Enable blob metadata extraction in the indexer.
Why it's wrong here
Not required for incremental enrichment.
- ✗
Add a custom skill that outputs a hash of the document content.
Why it's wrong here
Not required.
- ✓
Define a projection in the skillset to store enriched data.
Why this is correct
Projection stores intermediate state.
- ✓
Set the 'cacheKey' property in the skillset to a unique document identifier.
Why this is correct
Cache key identifies documents for incremental updates.
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