ARA-C01 · Question #108
ARA-C01 Question #108: Real Exam Question with Answer & Explanation
The correct answer is D: Use a multi-cluster warehouse in auto-scale mode with 1 size Medium cluster, and set. The most cost-effective way to increase the availability and performance of the reports during peak usage times, while keeping costs under control, is to use a multi-cluster warehouse in auto- scale mode. Option D suggests using a multi-cluster warehouse with 1 size Medium cluste
Question
An Architect needs to improve the performance of reports that pull data from multiple Snowflake tables, join, and then aggregate the data. Users access the reports using several dashboards. There are performance issues on Monday mornings between 9:00am-11:00am when many users check the sales reports. The size of the group has increased from 4 to 8 users. Waiting times to refresh the dashboards has increased significantly. Currently this workload is being served by a virtual warehouse with the following parameters: AUTO-RESUME = TRUE AUTO_SUSPEND = 60 SIZE = Medium What is the MOST cost-effective way to increase the availability of the reports?
Options
- AUse materialized views and pre-calculate the data.
- BIncrease the warehouse to size Large and set auto_suspend = 600.
- CUse a multi-cluster warehouse in maximized mode with 2 size Medium clusters.
- DUse a multi-cluster warehouse in auto-scale mode with 1 size Medium cluster, and set
Explanation
The most cost-effective way to increase the availability and performance of the reports during peak usage times, while keeping costs under control, is to use a multi-cluster warehouse in auto- scale mode. Option D suggests using a multi-cluster warehouse with 1 size Medium cluster and allowing it to auto-scale between 1 and 4 clusters based on demand. This setup ensures that additional computing resources are available when needed (e.g., during Monday morning peaks) and are scaled down to minimize costs when the demand decreases. This approach optimizes resource utilization and cost by adjusting the compute capacity dynamically, rather than maintaining a larger fixed size or multiple clusters continuously.
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