Microsoft Azure AI Engineer Associate AI-102 Practice Question
During development of an Azure AI Content Understanding solution, you build a skillset that runs the OCR skill and then the entity recognition skill on image files stored in Azure Blob Storage. You must persist the recognized entities in a structured form that downstream Power BI reports can query directly. Which feature of Azure AI Search should you configure?
Configure a table projection in the knowledge store.
Enable semantic ranking for the index.
Set the indexer's enrichMode property to mergeOrUpload.
A knowledge store provides optional projections that write enrichment output to storage where it can be queried independently of the search index. A table projection writes structured data, such as entities extracted by the entity recognition skill, to a tabular format in Azure Table Storage or Azure Cosmos DB. This satisfies the requirement to persist entities in a structured, query-ready form. Synonym maps, enrichMode settings, and semantic ranking affect search behavior in the index itself and do not create a separate, structured store of enrichment results.
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Microsoft Azure AI Engineer Associate AI-102
Implement knowledge mining and information extraction solutions
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