BenchMark Quizzes by MDFT Pro

Enable Full-Text Document Content Search

You work as a Cloud Search Engineer for MDFT Pro, a well-known training agency that delivers certification courses to students worldwide. Claire, the Document Management Lead, is building a comprehensive document search system that handles millions of student documents stored in Azure Blob Storage across hundreds of containers. The documents include course PDFs, student transcript CSVs, certification records in Office formats, and course feedback in plain text files. Customers upload documents that must be categorized by customer identifier for filtering purposes. The system must support two critical capabilities: users need to filter documents by customer identifier to find all documents for a specific customer, and they must be able to search for specific information contained within the document content itself—such as finding all transcripts mentioning “Azure Administrator” or all feedback containing “excellent instructor”. The solution must handle millions of documents cost-effectively. You’ve created a standard general-purpose v2 storage account to support this solution.

How should you enable users to search information inside documents?

Choose the correct answer from the options below.

Explanations for each answer:

Azure Cognitive Search is correct. Azure Cognitive Search (Azure AI Search) provides full-text search capabilities for searching information contained within document content—extracting and indexing text from PDF, CSV, Microsoft Office, and plain text files. The blob indexer automatically extracts content and metadata from blobs, making them searchable through a search index. While it requires provisioning a separate search service, it's the only option that enables searching inside document content, which is an explicit requirement.
Azure blob index tags is incorrect. Blob index tags provide native filtering and categorization for blobs using key-value pairs like 'customerId=12345', allowing you to filter blobs by customer identifier across containers. However, index tags only work with blob metadata and properties—they cannot search or index the information contained within documents themselves. Index tags solve the filtering requirement but not the content search requirement.
Azure blob inventory policy is incorrect. Blob inventory policies generate scheduled reports (CSV or Parquet files) listing blobs and their properties for auditing and compliance purposes. Inventory reports provide metadata about blobs but do not enable searching the content inside documents. This feature addresses neither the content search requirement nor the real-time filtering by customer identifier.
Azure blob metadata is incorrect. Blob metadata allows storing custom key-value pairs with each blob, including customer identifiers. However, metadata only describes the blob—it doesn't enable searching the actual content within PDF, CSV, Office, or text files. Additionally, metadata cannot be searched efficiently across millions of blobs without listing and checking each blob individually, making it unsuitable for both requirements.

Learn more about Blob Storage Integration:

Azure AI Search Blob Indexing

Practice Exam

Enable Full-Text Document Content Search

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