DBS-C01 · Question #59
A company has a production Amazon Aurora Db cluster that serves both online transaction processing (OLTP) transactions and compute-intensive reports. The reports run for 10% of the total cluster uptim
The correct answer is D. Set up automatic scaling on the DB cluster. This will allow the number of reader nodes to adjust. Explanation Option D is correct because Amazon Aurora supports Auto Scaling for Aurora Replicas, which automatically adjusts the number of reader nodes based on actual workload demand. This means the cluster can scale up to six nodes during peak periods (when both OLTP and report
Question
A company has a production Amazon Aurora Db cluster that serves both online transaction processing (OLTP) transactions and compute-intensive reports. The reports run for 10% of the total cluster uptime while the OLTP transactions run all the time. The company has benchmarked its workload and determined that a six-node Aurora DB cluster is appropriate for the peak workload. The company is now looking at cutting costs for this DB cluster, but needs to have a sufficient number of nodes in the cluster to support the workload at different times. The workload has not changed since the previous benchmarking exercise. How can a Database Specialist address these requirements with minimal user involvement?
Options
- ASplit up the DB cluster into two different clusters: one for OLTP and the other for reporting.
- BReview all evaluate the peak combined workload. Ensure that utilization of the DB cluster node is
- CUse the stop cluster functionality to stop all the nodes of the DB cluster during times of minimal
- DSet up automatic scaling on the DB cluster. This will allow the number of reader nodes to adjust
How the community answered
(18 responses)- A6% (1)
- B6% (1)
- C11% (2)
- D78% (14)
Explanation
Explanation
Option D is correct because Amazon Aurora supports Auto Scaling for Aurora Replicas, which automatically adjusts the number of reader nodes based on actual workload demand. This means the cluster can scale up to six nodes during peak periods (when both OLTP and reporting workloads are running) and scale down during off-peak times, directly reducing costs while maintaining performance - all without manual intervention, satisfying the "minimal user involvement" requirement.
Option A is incorrect because splitting into two clusters would increase costs by duplicating infrastructure, not reduce them. Option B is incomplete/vague and merely suggests reviewing utilization without providing an actionable, automated solution. Option C is incorrect because stopping the entire cluster during "minimal" activity would also take down the always-running OLTP transactions, which cannot tolerate downtime.
Memory Tip: Think "Auto Scaling = Auto Savings." Whenever an exam question mentions fluctuating workloads, cost reduction, and minimal user involvement together in an Aurora context, Aurora Auto Scaling for read replicas is almost always the answer - it handles the scaling decisions for you based on CloudWatch metrics like CPU or connection count.
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