The workload is a classic “hot key / hot dataset” pattern: many reads repeatedly target a small subset of items. The best way to improve performance and reduce latency is to add a caching layer so the application does not hit DynamoDB for every read.
Option B is purpose-built for DynamoDB read acceleration: DynamoDB Accelerator (DAX) is an in-memory cache that sits in front of DynamoDB and is API-compatible for many DynamoDB operations. DAX can dramatically reduce read latency (often to microseconds) for frequently accessed items and offload read traffic from the table.
Option A can also help: ElastiCache (Redis/Memcached) can be used as an application-managed cache. This is useful when the application wants more control over caching strategy, TTLs, and non-DynamoDB data caching. For ECS applications, ElastiCache is a common high-performance caching choice.
Why not the others:
C (strongly consistent reads) typically increases latency and capacity consumption; it does not improve performance for repeated reads.
D (increase read capacity) can reduce throttling, but it does not reduce latency as effectively as caching and can be more expensive for hot-read patterns.
E (adaptive capacity) helps DynamoDB handle uneven workloads, but it is not a direct performance boost like caching for repeated reads of a small dataset.
Therefore, adding caching via ElastiCache and/or DAX best improves performance. With “select two,” A and B are correct.
