COF-C03 Question 275
Single answerGrouping similar workloadsA Snowflake administrator is reviewing warehouse usage for a data platform that supports several teams. The current design uses one large virtual warehouse for all workloads, including short dashboard queries from BI users, hourly ELT transformations, and occasional ad hoc data science exploration. During business hours, dashboard users report inconsistent performance whenever ELT jobs are running. The administrator wants to improve both performance predictability and cost visibility by grouping similar workloads. Which approach is the BEST recommendation?
- A
Create separate virtual warehouses for BI dashboards, ELT jobs, and data science workloads so each workload type can scale and be monitored independently
- B
Keep a single warehouse, but increase its size so all workloads can run together without queueing
- C
Move all dashboard queries and ELT jobs into the same resource monitor so Snowflake can prioritize similar statements automatically
- D
Use one multi-cluster warehouse for all workloads because multi-cluster warehouses are primarily designed to separate workload types
Show answer and explanation
Correct answer: A
Explanation
The best recommendation is to separate unlike workloads into different virtual warehouses. Snowflake best practices emphasize using warehouses to isolate compute for workloads with different characteristics, such as ETL/ELT, reporting, and ad hoc analytics. This improves performance isolation, allows independent sizing and scaling, and makes it easier to attribute costs by team or function. A larger single warehouse or a multi-cluster warehouse can help with some performance symptoms, especially concurrency, but they do not replace the architectural benefit of grouping similar workloads together. Resource monitors are for credit governance, not workload prioritization. Relevant Snowflake guidance includes virtual warehouse sizing/scaling concepts, workload isolation practices, and using separate warehouses for distinct workload patterns.
- A. Correct.
Correct. A core Snowflake best practice is to group similar workloads onto separate virtual warehouses. BI queries, ELT processing, and exploratory data science activity have different concurrency, latency, and compute patterns. Isolating them into separate warehouses improves workload isolation, gives more predictable performance, and provides clearer chargeback/showback by warehouse. Each warehouse can then be sized, auto-suspended, and scaled according to its own usage characteristics.
- B. Incorrect.
Incorrect. Increasing warehouse size may reduce some contention, but it does not address the underlying problem of mixed workload patterns competing for the same compute resources. It also reduces cost visibility because all teams still consume from one warehouse. This is a common misconception: bigger compute can help performance, but workload isolation is the better design when different job types interfere with each other.
- C. Incorrect.
Incorrect. Resource monitors are used to track and control credit consumption at the warehouse or account level; they do not prioritize statements or automatically group similar workloads. Someone might choose this option because resource monitors are related to cost governance, but they are not a workload management or prioritization feature.
- D. Incorrect.
Incorrect. Multi-cluster warehouses help handle concurrency by adding clusters for many simultaneous queries, which is often useful for high-concurrency BI workloads. However, they are not primarily intended to separate different workload types that should be isolated for performance and cost management. Using one multi-cluster warehouse for everything can still mix ELT, BI, and ad hoc workloads in ways that reduce predictability.