Deploy Amazon ElastiCache for Redis and have the application lazily populate cached catalog query results is the correct choice because an in memory cache serves frequent catalog reads with far lower latency and it offloads repeated query work from the single AZ RDS instance.

Deploy Amazon ElastiCache for Redis and have the application lazily populate cached catalog query results reduces database CPU by returning results from memory after a cache miss populates the cache. With catalog data changing only a few times per week a cache aside pattern with sensible TTLs and explicit invalidation on updates will keep cached entries fresh and greatly lower latency during browsing and search.

Migrate the catalog to Amazon DynamoDB and add DynamoDB Accelerator (DAX) to cache catalog lookups is not ideal because it requires a major migration from a relational schema to NoSQL and it adds operational and development overhead that is unnecessary when the data is infrequently updated.

Create Amazon RDS read replicas and route catalog read traffic to the replicas from the frontend can increase read capacity but replicas still execute queries on database instances and may not eliminate CPU spikes or reduce latency as effectively as an in memory cache. Replication lag is also a potential concern for recently updated catalog items.

Move the catalog to Amazon Aurora Serverless v2 and rely on its scaling and caching to handle frequent queries involves moving database engines and it does not provide the same low latency for hot reads that a dedicated in memory cache delivers. Autoscaling compute helps concurrency but it does not replace caching for repeated lookups.

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