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Answer: Use multi-clustered warehousing to scale out warehouse capacity., Migrate some queries to a new warehouse to reduce load.
The question addresses persistent queuing of queries in a warehouse, indicating high concurrency issues. According to Snowflake documentation and community consensus (with AD having 81% support and higher upvotes), the recommended approaches are: A) Use multi-clustered warehousing to scale out warehouse capacity, which adds clusters to handle concurrent queries, and D) Migrate some queries to a new warehouse to reduce load, which immediately alleviates queuing. Option B (scale up warehouse size) is less suitable as it addresses query performance for complex queries rather than concurrency, and the issue is persistent queuing, not slow execution. Option C (stop/start warehouse) is disruptive and doesn't resolve the underlying concurrency problem. Option E (limit user access) is not a best practice as it restricts functionality rather than optimizing resources.
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When monitoring the load for a warehouse, the load monitoring chart shows that a high volume of queries is consistently queued.
Based on recommended best practices, which two actions should be taken to reduce the queue volume?
A
Use multi-clustered warehousing to scale out warehouse capacity.
B
Scale up the warehouse size to allow queries to execute faster.
C
Stop and start the warehouse to clear the queued queries.
D
Migrate some queries to a new warehouse to reduce load.
E
Limit user access to the warehouse so fewer queries are run against it.