Concurrency Scaling automatically adds temporary cluster capacity when many queries arrive at once, so query performance stays consistent during spikes — then removes it when demand drops.
The tell
When a scenario is “Redshift queries slow down or queue up during peak concurrent usage,” the fix is Concurrency Scaling: it spins up extra capacity for the burst automatically. You get free credits for it each day, and pay only beyond that. Consistent performance under concurrent query spikes → Concurrency Scaling.
Test yourself
A Redshift cluster’s queries slow down and queue during periods of high concurrent usage. What automatically maintains performance during those bursts?
- Add more distribution keys
- Enable Concurrency Scaling
- Switch to DynamoDB
- Reduce the retention period
👉 Click to reveal the answer & explanation
Correct answer: B. Concurrency Scaling adds temporary capacity automatically during concurrency spikes to keep performance consistent. Distribution keys (A) optimize joins, not concurrency; DynamoDB (C) isn’t a warehouse; retention (D) is unrelated.
Related topics
Amazon Redshift · Redshift distribution keys · Redshift Serverless
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