Snowflake ARA-C01 Practice Test - Questions Answers, Page 17
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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?
Use materialized views and pre-calculate the data.
Increase the warehouse to size Large and set auto_suspend = 600.
Use a multi-cluster warehouse in maximized mode with 2 size Medium clusters.
Use a multi-cluster warehouse in auto-scale mode with 1 size Medium cluster, and set min_cluster_count = 1 and max_cluster_count = 4.
An Architect is designing a solution that will be used to process changed records in an orders table. Newly-inserted orders must be loaded into the f_orders fact table, which will aggregate all the orders by multiple dimensions (time, region, channel, etc.). Existing orders can be updated by the sales department within 30 days after the order creation. In case of an order update, the solution must perform two actions:
1. Update the order in the f_0RDERS fact table.
2. Load the changed order data into the special table ORDER _REPAIRS.
This table is used by the Accounting department once a month. If the order has been changed, the Accounting team needs to know the latest details and perform the necessary actions based on the data in the order_repairs table.
What data processing logic design will be the MOST performant?
Use one stream and one task.
Use one stream and two tasks.
Use two streams and one task.
Use two streams and two tasks.
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