Which combination of steps should the solutions architect take to meet these requirements?

Explanations:

Building a Docker image and publishing it to Amazon ECR allows the application to run in a containerized environment with custom dependencies. This setup supports larger processing tasks and is compatible with ECS, allowing scalable processing without infrastructure management.

Creating an ECS task definition with Fargate allows the application to run without underlying infrastructure management, and invoking the task via Lambda reduces load and timeout issues. Fargate is serverless, enabling it to handle larger image processing workloads seamlessly without manual scaling.

Increasing provisioned concurrency for the Lambda function does not address the issue of exceeding maximum runtime, which is a core limitation with the larger image sizes. AWS Step Functions would not mitigate Lambda’s runtime limit, as the Lambda execution would still face timeout issues on large tasks.

Using Amazon ECS with EC2 rather than Fargate does not meet the requirement to avoid managing underlying infrastructure, as EC2 instances would require management for scaling and maintenance, which is undesirable for the company. Fargate is a better fit for a serverless approach that requires no infrastructure management.

Storing images on Amazon EFS and metadata on RDS adds unnecessary complexity to the architecture and does not address the runtime limitation issue of Lambda. Additionally, using EFS and RDS would require infrastructure management, which is against the company’s requirements to avoid managing underlying infrastructure.