A Data Engineer needs to optimize the performance and minimize the costs of a dynamic table pipeline in Snowflake. The Engineer wants to monitor and separate the dynamic table costs prior to…

Question

Options

• AUse a shared virtual warehouse to test the performance of the dynamic table refresh process and
• BBefore moving the table, test the process on a dedicated virtual warehouse to establish a cost
• CExecute the dynamic tables on multiple virtual warehouses simultaneously to distribute the load
• DSet up the dynamic tables directly on a production virtual warehouse to obtain real-time cost

• A
  3% (2)
• B
  84% (57)
• C
  9% (6)
• D
  4% (3)

Explanation

Option B is correct because isolating the dynamic table refresh on a dedicated virtual warehouse first gives you a clean, uncontaminated cost baseline - no other workloads share the compute, so you can accurately measure refresh costs before moving to a shared environment where cost attribution becomes murky.

• A is wrong because starting on the shared warehouse defeats the purpose: you can't separate dynamic table costs from other workloads running on that same warehouse, which is exactly what the question says the engineer needs to do before transferring.
• C is wrong because distributing load across multiple warehouses simultaneously adds complexity and cost without establishing the isolated baseline the engineer requires; it also doesn't solve the cost-monitoring problem.
• D is wrong because testing directly on a production warehouse risks impacting production workloads and still doesn't isolate costs - production warehouses serve many processes simultaneously.

Memory tip: Think "measure before you merge." Just like you'd benchmark code in isolation before deploying to a shared environment, always benchmark dynamic table refresh on a dedicated warehouse first so your cost numbers are clean, then migrate to shared compute once you know what you're working with.

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