DAS-C01 · Question #67
A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a…
The correct answer is A. Enable concurrency scaling in the workload management (WLM) queue. Concurrency Scaling in Redshift's Workload Management (WLM) automatically adds transient read capacity when query queues build up and removes it when demand drops. You are charged only for the seconds of concurrency scaling used, making it very cost-effective for predictable…
Question
A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime. What is the MOST cost-effective solution?
Options
- AEnable concurrency scaling in the workload management (WLM) queue.
- BAdd more nodes using the AWS Management Console during peak hours. Set the distribution
- CUse elastic resize to quickly add nodes during peak times. Remove the nodes when they are not
- DUse a snapshot, restore, and resize operation. Switch to the new target cluster.
How the community answered
(33 responses)- A82% (27)
- B3% (1)
- C9% (3)
- D6% (2)
Explanation
Concurrency Scaling in Redshift's Workload Management (WLM) automatically adds transient read capacity when query queues build up and removes it when demand drops. You are charged only for the seconds of concurrency scaling used, making it very cost-effective for predictable short peak windows. Options B and C (adding nodes via console or elastic resize) require manual intervention and you pay for those nodes even when idle. Option D (snapshot/restore/resize) causes downtime, which the requirement explicitly prohibits.
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