A highly available relational database with minimal manual intervention is best delivered by Amazon RDS Multi-AZ, which provides managed standby infrastructure and automatic failover. For the container-based application layer, Amazon ECS with the Fargate launch type reduces operational effort because AWS manages the underlying compute capacity for the containers. That combination provides managed availability for both the database and the application tier. Read replicas can help scale reads, but they do not replace Multi- AZ as the primary high-availability pattern for the database. ECS on EC2 is workable, but it requires more instance management than Fargate. This makes A and D the strongest pair for high availability with minimal operational overhead.

Amazon ElastiCache (Redis OSS) provides an in-memory cache that drastically improves read performance with minimal operational overhead.
It integrates easily with RDS and does not require schema or database changes.
EC2-based caching (Option C) increases management overhead.
DAX (Option D) accelerates DynamoDB only, not RDS.
Moving to DynamoDB (Option A) requires complete application and schema redesign.

Amazon ECS with AWS Fargate provides serverless container compute that can scale tasks dynamically in response to demand. This matches the unpredictable scaling requirements of a gaming workload. Aurora Serverless v2 provides on-demand, autoscaling relational database capacity while supporting complex SQL queries. EC2 with Auto Scaling (A) works but requires significant management overhead. Lambda with DynamoDB (B) is not suitable because the workload requires a relational database and long-running processes. ParallelCluster with HPC (D) is designed for batch scientific workloads, not dynamic, interactive gaming. Option C provides the correct balance of elasticity, performance, and managed services for both compute and relational database needs.
References:* Amazon ECS User Guide - Scaling ECS tasks with Fargate* Amazon Aurora User Guide - Aurora Serverless v2 for dynamic workloads

Amazon EFS Lifecycle Management enables automatic cost optimization by transitioning files that haven't been accessed for a defined period (e.g., 7 days) from EFS Standard to EFS Infrequent Access (IA).
"Amazon EFS Lifecycle Management automatically moves files that haven't been accessed for a set period to the EFS Infrequent Access storage class, reducing storage costs for infrequently accessed files."
- Amazon EFS Documentation
Key Points:
EFS IA is ideal for files larger than 128 KB and accessed less frequently.
It's seamless - no code or tools needed.
Meets requirement for cost optimization and high availability.
Incorrect Options:
A: File Gateway adds unnecessary complexity and does not use EFS.
B: Storing files locally breaks shared access and resiliency.
D: Glacier Deep Archive is cold storage - not "readily available."
References:
EFS Lifecycle Management
EFS IA Storage Class

AWS Secrets Manager is the best fit because it is built specifically to store, manage, and rotate secrets such as database credentials. AWS documentation states that Secrets Manager supports automatic rotation for supported secret types and reduces the need for custom credential-rotation code. KMS encrypts keys and data but is not itself a secret-rotation store. Parameter Store can hold secure strings, but Secrets Manager is the AWS service purpose-built for secret lifecycle management and scheduled rotation. Because the question asks for the least development effort, using Secrets Manager with automatic rotation is the most direct and operationally efficient solution.