The best solution is to use Amazon DynamoDB Accelerator (DAX). DAX is a fully managed, highly available, in-memory caching service specifically designed for DynamoDB. It delivers microsecond response times for read-heavy workloads without requiring developers to change existing application logic, because it is API-compatible with DynamoDB. This makes it ideal when performance improvements are needed but minimal operational overhead is desired.

Here's why the other options are incorrect:

Using Amazon ElastiCache for Redis can improve performance, but it requires custom code changes to manage cache population, invalidation, and integration with DynamoDB. It is not API-compatible with DynamoDB and thus introduces more operational and development effort.

Replicating data using DynamoDB global tables is designed for multi-Region availability and low-latency access across geographies. While it improves performance for global applications, it does not inherently optimize local read performance and introduces more complexity and cost than needed for this specific requirement.

Using Amazon ElastiCache for Memcached with Auto Discovery is another general-purpose caching option, but like Redis, it requires additional logic to integrate with DynamoDB and manage consistency between the cache and the database. It also lacks some advanced features such as data persistence and replication offered by Redis or DAX.

Why this question matters: Many real-world AWS applications use DynamoDB, and understanding how to optimize read-heavy workloads using native AWS services like DAX is critical. This question tests your ability to balance performance with operational simplicity.

Exam tip: When faced with a read-heavy DynamoDB workload that must be improved without changing the application, DAX is usually the best fit. Be cautious with other caching solutions unless the question specifically mentions custom caching logic or external databases.

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