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SOA-C02 · Question #248

A company has an application that customers use to search for records on a website. The application's data is stored in an Amazon Aurora DB cluster. The application's usage varies by season and by day

The correct answer is B. Deploy an Aurora Replica for the DB cluster.. Caching will not solve the performance issue in this scenario, as the same search is rarely performed multiple times. Thus read replicas will be better. https://docs.amazonaws.cn/en_us/AmazonRDS/latest/AuroraUserGuide/aurora-replicas-

Submitted by khalil_dz· Mar 30, 2026Cost and Performance Optimization

Question

A company has an application that customers use to search for records on a website. The application's data is stored in an Amazon Aurora DB cluster. The application's usage varies by season and by day of the week. The website's popularity is increasing, and the website is experiencing slower performance because of increased load on the DB cluster during periods of peak activity. The application logs show that the performance issues occur when users are searching for information. The same search is rarely performed multiple times. A SysOps administrator must improve the performance of the platform by using a solution that maximizes resource efficiency. Which solution will meet these requirements?

Options

  • ADeploy an Amazon ElastiCache for Redis cluster in front of the DB cluster.
  • BDeploy an Aurora Replica for the DB cluster.
  • CUse Provisioned IOPS on the storage volumes that support the DB cluster to improve
  • DIncrease the instance size in the DB cluster to a size that is sufficient to support the peak load on

How the community answered

(51 responses)
  • A
    2% (1)
  • B
    84% (43)
  • C
    10% (5)
  • D
    4% (2)

Explanation

Caching will not solve the performance issue in this scenario, as the same search is rarely performed multiple times. Thus read replicas will be better. https://docs.amazonaws.cn/en_us/AmazonRDS/latest/AuroraUserGuide/aurora-replicas-

Topics

#Aurora Replica#read scaling#DB performance optimization#peak load handling

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