SOL-C01 Question #241: Real Exam Question with Answer & Explanation

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

Explanation

Optimizing SQL queries (Option D) is crucial for reducing compute costs. Efficient queries consume fewer resources, potentially reducing the need for a larger warehouse. Switching to Serverless tasks (Option E) is also highly effective, as Snowflake automatically manages compute resources and only charges for actual usage. Options A and B are less effective. Reducing the warehouse size (A) will increase processing time, negating the cost savings. Auto- suspending (B) is good practice, but doesn't directly address the root cause. Prioritizing queries (C) is a good strategy but might not be as impactful as optimizing queries or using Serverless

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