Practice Exam

Explanations for each answer:

• Azure Cognitive Search is incorrect. Azure Cognitive Search (now Azure AI Search) provides full-text search capabilities for searching content within documents—the actual text, images, and data inside PDF, CSV, Office, and plain text files. While powerful for content search, it requires provisioning a separate search service, creating indexes, and running indexers, which increases costs. The requirement to 'minimize costs' and the need for simple filtering by customer identifier makes blob index tags more cost-effective than a full search service.
• Azure blob index tags is correct. Blob index tags provide native, cost-effective filtering and categorization directly in Blob Storage without external services. You can tag blobs with key-value pairs like 'customerId=12345' as they're uploaded, then use the Find Blobs by Tags API to filter millions of blobs across hundreds of containers by customer identifier instantly. Index tags are automatically indexed by Azure Storage, support cross-container queries, and minimize costs since they're a built-in feature requiring no additional services or complex infrastructure.
• Azure blob inventory policy is incorrect. Blob inventory policies generate scheduled reports (CSV or Parquet files) listing blobs and their properties, useful for auditing and compliance. However, inventory reports are generated periodically (daily or weekly), not in real-time, and don't provide a query interface for filtering blobs by customer identifier. To filter by customer, you'd need to process inventory files separately, which adds complexity and latency compared to blob index tags' real-time filtering capability.
• Azure blob metadata is incorrect. Blob metadata allows storing custom key-value pairs with each blob, including customer identifiers. However, metadata cannot be searched or filtered natively by Azure Storage—you must list all blobs and check each blob's metadata individually, which is extremely inefficient for millions of documents across hundreds of containers. Blob index tags solve this problem by providing indexed, searchable tags specifically designed for filtering scenarios.