This workload has two distinct needs: (1) a managed way for external clients to upload files using a familiar managed file transfer protocol, and (2) an automated, low-ops method to run a watermarking step and store the resulting images as objects. AWS Transfer Family provides a fully managed capability to receive files over protocols such as SFTP, while landing those files directly into Amazon S3 as objects. That directly satisfies the requirement that “uploaded images must be stored as objects” with minimal infrastructure management.

To automate watermarking with minimal operational overhead, invoking AWS Lambda is a strong fit. Lambda is serverless and scales automatically with incoming uploads, and it can run the watermarking logic as code without managing servers. Transfer Family supports managed workflows that can orchestrate post-upload actions; using a Transfer Family workflow to invoke a Lambda function provides a clean, managed, event-driven pipeline: clients upload via SFTP → objects land in S3 → workflow triggers watermark processing → output is written back to S3.

Option A adds operational burden by requiring an EC2-based application tier for watermarking, which increases patching and scaling responsibilities. Option B can work (S3 + Batch), but it requires building and operating the upload mechanism (REST APIs) and typically more orchestration than necessary for simple “upload then process” flows; Batch is better when you need large-scale, long-running batch compute rather than lightweight per-object processing. Option D is unsuitable because S3 Glacier Deep Archive is intended for long-term archival with slow retrieval, which conflicts with active workflow processing and watermark automation.

Therefore, C best meets the requirements using managed services end-to-end: Transfer Family for ingestion, S3 for object storage, and Lambda for automated watermarking.