SOA-C03 - Reliability and Business Continuity - Section 2.2

Scale managed databases and add caching using Amazon RDS and Aurora scaling, DynamoDB capacity modes, Amazon ElastiCache and Amazon CloudFront.

Scale managed databases by enabling Aurora Auto Scaling for read replicas, choosing between DynamoDB on-demand and provisioned auto scaling capacity modes, and offloading repeated reads with Amazon ElastiCache or CloudFront caching. Distinguish which approach reduces latency versus reduces cost for a given access pattern.

Aurora Auto ScalingDynamoDB auto scalingAmazon ElastiCacheCloudFront caching

Practice question for this objective

Free sampleReliability and Business Continuitymedium

An Aurora MySQL-Compatible cluster serves a reporting portal whose read traffic climbs sharply each weekday morning and falls away by mid-afternoon. The writer instance stays lightly loaded, but read latency rises during the peak, and the same popular dashboard queries run thousands of times an hour against rows that change only a few times a day. The team must absorb the morning read surge and cut repeated read pressure on the cluster while adding as little ongoing tuning as possible. Which two actions, taken together, meet this requirement? (Select TWO.)

  • AEnable Aurora Replica Auto Scaling so reader instances are added and removed automatically against a target metric, with reads served from the cluster reader endpoint. Correct
  • BConvert the cluster to a Multi-AZ deployment so a standby in a second Availability Zone serves the additional morning read traffic alongside the writer.
  • CIncrease the writer instance class to a larger size so it has the capacity to handle the extra morning read queries without any replicas.
  • DPlace an Amazon ElastiCache cache in front of the cluster and serve the popular dashboard query results from it so repeated reads are answered without touching Aurora. Correct
  • ESwitch the cluster volume to Provisioned IOPS storage so the morning read queries complete faster under the higher disk throughput.
Aurora read scaling combines Replica Auto Scaling for elastic reader capacity with an ElastiCache layer that absorbs repeated reads of slow-changing data. The requirement has two parts: absorb an elastic read surge and cut repeated read load with little tuning. Replica Auto Scaling adds and removes readers automatically to follow the daily curve, while caching the hot, rarely changing query results in ElastiCache answers most repeat reads without reaching Aurora. Multi-AZ is availability not read scaling, enlarging the writer is manual vertical sizing, and Aurora has no Provisioned IOPS volume to tune.

Why A is correct: Replica Auto Scaling grows and shrinks the reader fleet on its own to track the daily read surge, spreading read traffic with minimal tuning.

Why B is wrong: Multi-AZ delivers in-Region failover availability and the standby does not serve normal read traffic, so it does nothing to scale reads.

Why C is wrong: Scaling the writer up is manual vertical sizing for steady load, wasteful overnight, and it leaves repeated reads hitting the database directly.

Why D is correct: Caching the slow-changing hot query results offloads the bulk of repeated reads from the cluster, directly cutting read pressure during the peak.

Why E is wrong: Aurora manages its own distributed storage and does not use Provisioned IOPS volumes, so this setting does not exist and cannot scale reads.

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