A retail company has 2000+ stores spread across the country. Store Managers report that they are having trouble running key reports related to inventory management, sales targets, payroll, and staffin

Question

Options

• AUse a Business Intelligence tool for in-memory computation to improve performance.
• BConfigure a dedicated virtual warehouse for the Store Manager team.
• CConfigure the virtual warehouse to be multi-clustered.
• DConfigure the virtual warehouse to size 4-XL
• EAdvise the Store Manager team to defer report execution to off-business hours.

• A
  4% (2)
• B
  73% (35)
• D
  8% (4)
• E
  15% (7)

Explanation

With 2000+ store managers all sharing one warehouse, there are two distinct problems: workload isolation (their queries compete with other teams) and concurrency (hundreds of simultaneous dashboard queries from managers across the country saturate a single-cluster warehouse). Option B (dedicated virtual warehouse for the Store Manager team) solves isolation - their queries no longer contend with other teams' workloads for compute credits and queue slots. Option C (multi-cluster warehouse) solves concurrency - when many managers run reports simultaneously, Snowflake automatically adds compute clusters to service queued queries in parallel instead of making them wait. Option A (BI tool for in-memory computation) offloads some processing but does not fix the Snowflake warehouse bottleneck. Option D (4-XL size) increases per-query performance for complex queries but a larger single cluster does not increase the number of concurrent queries that can run without queuing. Option E (defer to off-hours) is operationally unacceptable - store managers need real-time inventory and payroll data during business hours.

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