You are designing a storage architecture for a data analytics solution that requires low-latency access for frequently used data and cost-effective storage for archival data. What approach optimizes both requirements?

Correct answer: Implement tiered storage using hot tier for active data, cool tier for infrequent access, and archive tier for long-term retention

Option C is correct because Azure Blob Storage tiered storage, using hot, cool, and archive tiers, directly matches the requirement: the hot tier provides low-latency, high-throughput access for frequently read active data, the cool tier reduces storage costs for data accessed infrequently, and the archive tier offers the lowest cost for long-term retention where retrieval latency is acceptable. Option A, using Azure Data Lake Storage Gen2 standard tier for all data, does not optimize costs because it applies a single pricing tier regardless of access frequency, missing the archival cost savings. Option B, storing all data on Azure Premium SSD, is extremely cost-prohibitive for archival data and is designed for high-IOPS workloads like databases, not bulk analytics storage. Option D, archiving all data to Azure Backup immediately, is wrong because Azure Backup is a disaster recovery and backup service, not a primary analytics storage tier, and immediately archiving active data would make it inaccessible for analytics.