A company that runs seasonal job boards is experiencing increased traffic. The backend consists of two Amazon EC2 instances behind an Application Load Balancer, and Amazon DynamoDB is used as the datastore. During peak seasons, application read and write performance degrades. Which option yields a scalable architecture that can handle peak loads with the LEAST development effort?

The correct option scales both the compute and the database layers with minimal development effort. Auto Scaling groups automatically adjust the number of EC2 instances based on demand, ensuring the application can handle increased traffic. DynamoDB auto scaling dynamically adjusts read and write capacity units, preventing performance degradation during peak loads. Migrating to AWS Lambda (options A and B) would require significant re-architecting and development effort, as the existing EC2-based backend would need to be re-written as serverless functions. Increasing DynamoDB read/write capacity manually (option A) is not scalable as it requires constant monitoring and adjustment. DynamoDB global tables (option B) are for multi-region replication and disaster recovery, not for scaling within a single region. Using SQS and Lambda for writes (option D) adds complexity and latency, and while it can help decouple writes, it doesn't directly address the read performance degradation or the scaling of the existing EC2 instances.