SOL-C01 · Question #241
A company utilizes Snowflake's elastic compute capabilities for its daily batch processing jobs. They have noticed that the warehouse consistently scales up to X-Large during the peak processing windo
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Question
A company utilizes Snowflake's elastic compute capabilities for its daily batch processing jobs. They have noticed that the warehouse consistently scales up to X-Large during the peak processing window (8 PM to 10 PM PST) and then scales back down. However, the cost of running the X-Large warehouse for these 2 hours is becoming significant. They are considering optimizing their compute costs. Which of the following strategies would be the MOST effective in reducing compute costs without significantly impacting the overall processing time of the batch jobs?
Options
- AReduce the warehouse size to Large to decrease the hourly compute cost. While this will reduce
- BEnable auto-suspend for the warehouse with a shorter timeout period (e.g., 5 minutes). This will
- CImplement workload management rules to prioritize critical queries during the peak processing
- DAnalyze the SQL queries executed during the batch jobs and identify inefficient queries that
- ESwitch to using Snowflake's Serverless tasks for the batch processing jobs. Serverless tasks
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