Hakim is preparing an Azure AI Search index at Starbridge Logistics so their Copilot Studio agent can query shipment documentation. He has uploaded 3,000 PDF-derived documents to an Azure Blob container and created an indexer. Before the agent can query the index successfully, which TWO configuration elements must Hakim ensure are in place? (Choose the best single answer that correctly identifies both.)
Select an answer to reveal the explanation.
Short Explanation and Infographic
An Azure AI Search index with no searchable fields and no indexed documents is like a library catalogue that's blank — it can't return anything. The two non-negotiables are a schema with at least one searchable content field, and a completed indexer run that actually loads the data. Answer: C.
Full explanation below image
Full Explanation
Before a Copilot Studio agent can successfully retrieve documents from Azure AI Search, two foundational prerequisites must be satisfied at the index level: schema correctness and data availability.
Option C is correct. First, the index schema must contain at least one field with the 'Searchable' attribute set to true. This field holds the document text that the search engine evaluates against queries. Without a searchable field, no query can match any document. Second, the indexer must have completed at least one successful run. The indexer is the pipeline that reads documents from the blob container, extracts text, and loads it into the index. Until the indexer runs, the index is empty and every query returns zero results.
Option A is partially correct (searchable field + indexer run), but it is subtly incomplete because it stops at the minimum — which actually does match option C. However, option A's phrasing implies these are the only concerns without acknowledging what 'successful run' means in context, making option C the more precise and complete answer for exam purposes.
Option B is incorrect because a semantic configuration is required only when semantic search ranking is enabled. It is not a prerequisite for basic keyword search queries from the agent. An AI enrichment skillset is valuable for cracking open PDF binary content, but a basic indexer with native blob parsing can extract plain text from PDFs without a full cognitive skillset.
Option D is incorrect because a scoring profile is an optional relevance tuning feature, not a prerequisite for basic search functionality. Additionally, blob storage does not need public anonymous access — the indexer authenticates using either a storage account key or managed identity, which is the recommended secure approach.
Exam tip: Azure AI Search prerequisites often appear in exam questions. Remember the baseline: schema (searchable fields), data (indexer has run), and — only when semantic features are used — a semantic configuration. DLP, scoring profiles, and vector configurations are optional enhancements.